diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 0000000..0776c14 --- /dev/null +++ b/.editorconfig @@ -0,0 +1,15 @@ +root = true + +[*.py] +charset = utf-8 +trim_trailing_whitespace = true +end_of_line = lf +insert_final_newline = true +indent_style = space +indent_size = 4 + +[*.md] +trim_trailing_whitespace = false + +[*.yaml] +indent_size = 2 diff --git a/.github/workflows/pre-commit.yaml b/.github/workflows/pre-commit.yaml new file mode 100644 index 0000000..1790b3e --- /dev/null +++ b/.github/workflows/pre-commit.yaml @@ -0,0 +1,22 @@ +name: pre-commit +on: [push, pull_request] + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + pre-commit: + runs-on: ubuntu-22.04 + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.8 + uses: actions/setup-python@v4 + with: + python-version: '3.8' + - name: Install pre-commit + run: | + pip install pre-commit + pre-commit install + - name: Run pre-commit + run: pre-commit run --all-files diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c0cb4bf --- /dev/null +++ b/.gitignore @@ -0,0 +1,198 @@ +# Created by https://www.toptal.com/developers/gitignore/api/python +# Edit at https://www.toptal.com/developers/gitignore?templates=python + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Slurm logs +slurm* +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +### Python Patch ### +# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration +poetry.toml + +# ruff +.ruff_cache/ + +# LSP config files +pyrightconfig.json + +# End of https://www.toptal.com/developers/gitignore/api/python + +.vscode/ +.threestudio_cache/ +outputs/ +outputs-gradio/ + +# pretrained model weights +*.ckpt +*.pt +*.pth + +# wandb +wandb/ + +# vscode +.code-workspace + +# custom/* + +load/tets/256_tets.npz diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..4687d3c --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,34 @@ +default_language_version: + python: python3 + +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.4.0 + hooks: + - id: trailing-whitespace + - id: check-ast + - id: check-merge-conflict + - id: check-yaml + - id: end-of-file-fixer + - id: trailing-whitespace + args: [--markdown-linebreak-ext=md] + + - repo: https://github.com/psf/black + rev: 23.3.0 + hooks: + - id: black + language_version: python3 + + - repo: https://github.com/pycqa/isort + rev: 5.12.0 + hooks: + - id: isort + exclude: README.md + args: ["--profile", "black"] + + # temporarily disable static type checking + # - repo: https://github.com/pre-commit/mirrors-mypy + # rev: v1.2.0 + # hooks: + # - id: mypy + # args: ["--ignore-missing-imports", "--scripts-are-modules", "--pretty"] diff --git a/.pylintrc b/.pylintrc new file mode 100644 index 0000000..9580ea5 --- /dev/null +++ b/.pylintrc @@ -0,0 +1,7 @@ +disable=R,C + +[TYPECHECK] +# List of members which are set dynamically and missed by pylint inference +# system, and so shouldn't trigger E1101 when accessed. Python regular +# expressions are accepted. +generated-members=numpy.*,torch.*,cv2.* diff --git a/2dplayground.ipynb b/2dplayground.ipynb new file mode 100644 index 0000000..7cc97d9 --- /dev/null +++ b/2dplayground.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generate 2D Images using SDS\n", + "\n", + "This notebook demonstrates how to generate 2D images using SDS. It is a good way to test the guidance techniques." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import math\n", + "from tqdm import tqdm\n", + "import numpy as np\n", + "import random\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "from torch.optim.lr_scheduler import LambdaLR\n", + "import threestudio\n", + "import gc\n", + "import time\n", + "import io\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from ipywidgets import interact, IntSlider, Output\n", + "from IPython.display import display, clear_output\n", + "from PIL import Image\n", + "\n", + "def get_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_cycles: float = 0.5):\n", + "\n", + " def lr_lambda(current_step):\n", + " if current_step < num_warmup_steps:\n", + " return float(current_step) / float(max(1, num_warmup_steps))\n", + " progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))\n", + " return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))\n", + "\n", + " return LambdaLR(optimizer, lr_lambda, -1)\n", + "\n", + "def seed_everything(seed):\n", + " random.seed(seed)\n", + " os.environ['PYTHONHASHSEED'] = str(seed)\n", + " np.random.seed(seed)\n", + " torch.manual_seed(seed)\n", + " torch.cuda.manual_seed(seed)\n", + " \n", + "# To specify the gpu you want to use, we recommend to start the jupyter server with CUDA_VISIBLE_DEVICES=.\n", + "# threestudio.utils.base.get_device = lambda: torch.device('cuda:0') # hack the cuda device" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "prompt = \"An astronaut riding a horse in space\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# stable diffusion \n", + "config = {\n", + " 'max_iters': 1000,\n", + " 'seed': 42,\n", + " 'scheduler': 'cosine',\n", + " 'mode': 'latent',\n", + " 'prompt_processor_type': 'stable-diffusion-prompt-processor',\n", + " 'prompt_processor': {\n", + " 'prompt': prompt,\n", + " },\n", + " 'guidance_type': 'stable-diffusion-guidance',\n", + " 'guidance': {\n", + " 'half_precision_weights': False,\n", + " 'guidance_scale': 100.,\n", + " 'pretrained_model_name_or_path': 'runwayml/stable-diffusion-v1-5',\n", + " 'grad_clip': None,\n", + " 'view_dependent_prompting': False,\n", + " },\n", + " 'image': {\n", + " 'width': 64,\n", + " 'height': 64,\n", + " }\n", + "}\n", + "\n", + "# deepfloyd\n", + "\n", + "# config = {\n", + "# 'max_iters': 1000,\n", + "# 'seed': 42,\n", + "# 'scheduler': 'cosine',\n", + "# 'mode': 'rgb', # deepfloyd does not support latent optimization\n", + "# 'prompt_processor_type': 'deep-floyd-prompt-processor',\n", + "# 'prompt_processor': {\n", + "# 'prompt': prompt,\n", + "# },\n", + "# 'guidance_type': 'deep-floyd-guidance',\n", + "# 'guidance': {\n", + "# 'half_precision_weights': True,\n", + "# 'guidance_scale': 7.,\n", + "# 'pretrained_model_name_or_path': 'DeepFloyd/IF-I-XL-v1.0',\n", + "# 'grad_clip': None,\n", + "# \"view_dependent_prompting\": False,\n", + "# },\n", + "# 'image': {\n", + "# 'width': 64,\n", + "# 'height': 64,\n", + "# }\n", + "# }\n", + "\n", + "seed_everything(config['seed'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# just need to rerun the cell when you change guidance or prompt_processor\n", + "guidance = None\n", + "prompt_processor = None\n", + "gc.collect()\n", + "with torch.no_grad():\n", + " torch.cuda.empty_cache()\n", + "\n", + "guidance = threestudio.find(config['guidance_type'])(config['guidance'])\n", + "prompt_processor = threestudio.find(config['prompt_processor_type'])(config['prompt_processor'])\n", + "prompt_processor.configure_text_encoder()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def figure2image(fig):\n", + " buf = io.BytesIO()\n", + " fig.savefig(buf)\n", + " buf.seek(0)\n", + " img = Image.open(buf)\n", + " return img\n", + "\n", + "def configure_other_guidance_params_manually(guidance, config):\n", + " # avoid reloading guidance every time change these params\n", + " guidance.cfg.grad_clip = config['guidance']['grad_clip']\n", + " guidance.cfg.guidance_scale = config['guidance']['guidance_scale']\n", + "\n", + "def run(config):\n", + " # clear gpu memory\n", + " rgb = None\n", + " grad = None\n", + " vis_grad = None\n", + " vis_grad_norm = None\n", + " loss = None\n", + " optimizer = None\n", + " target = None\n", + "\n", + " gc.collect()\n", + " with torch.no_grad():\n", + " torch.cuda.empty_cache()\n", + " \n", + " configure_other_guidance_params_manually(guidance, config)\n", + "\n", + " mode = config['mode']\n", + " \n", + " w, h = config['image']['width'], config['image']['height']\n", + " if mode == 'rgb':\n", + " target = nn.Parameter(torch.rand(1, h, w, 3, device=guidance.device))\n", + " else:\n", + " target = nn.Parameter(torch.randn(1, h, w, 4, device=guidance.device))\n", + "\n", + " optimizer = torch.optim.AdamW([target], lr=1e-1, weight_decay=0)\n", + " num_steps = config['max_iters']\n", + " scheduler = get_cosine_schedule_with_warmup(optimizer, 100, int(num_steps*1.5)) if config['scheduler'] == 'cosine' else None\n", + "\n", + " rgb = None\n", + " plt.axis('off')\n", + "\n", + " img_array = []\n", + "\n", + " try:\n", + " for step in tqdm(range(num_steps + 1)):\n", + " optimizer.zero_grad()\n", + "\n", + " batch = {\n", + " 'elevation': torch.Tensor([0]),\n", + " 'azimuth': torch.Tensor([0]),\n", + " 'camera_distances': torch.Tensor([1]),\n", + " }\n", + "\n", + " loss = guidance(target, prompt_processor(), **batch, rgb_as_latents=(mode != 'rgb'))\n", + " loss['loss_sds'].backward()\n", + "\n", + " grad = target.grad\n", + " optimizer.step()\n", + " if scheduler is not None:\n", + " scheduler.step()\n", + " \n", + " guidance.update_step(epoch=0, global_step=step)\n", + "\n", + " if step % 5 == 0:\n", + " if mode == 'rgb':\n", + " rgb = target\n", + " vis_grad = grad[..., :3]\n", + " vis_grad_norm = grad.norm(dim=-1)\n", + " else:\n", + " rgb = guidance.decode_latents(target.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)\n", + " vis_grad = grad\n", + " vis_grad_norm = grad.norm(dim=-1)\n", + " \n", + " vis_grad_norm = vis_grad_norm / vis_grad_norm.max()\n", + " vis_grad = vis_grad / vis_grad.max()\n", + " img_rgb = rgb.clamp(0, 1).detach().squeeze(0).cpu().numpy()\n", + " img_grad = vis_grad.clamp(0, 1).detach().squeeze(0).cpu().numpy()\n", + " img_grad_norm = vis_grad_norm.clamp(0, 1).detach().squeeze(0).cpu().numpy()\n", + "\n", + " fig, ax = plt.subplots(1, 3, figsize=(15, 5))\n", + " ax[0].imshow(img_rgb)\n", + " ax[1].imshow(img_grad)\n", + " ax[2].imshow(img_grad_norm)\n", + " ax[0].axis('off')\n", + " ax[1].axis('off')\n", + " ax[2].axis('off')\n", + " clear_output(wait=True)\n", + " plt.show()\n", + " img_array.append(figure2image(fig))\n", + " except KeyboardInterrupt:\n", + " pass\n", + " finally:\n", + " # browse the result\n", + " print(\"Optimizing process:\")\n", + " images = img_array\n", + " \n", + " if len(images) > 0:\n", + " # Set up the widgets\n", + " slider = IntSlider(min=0, max=len(images)-1, step=1, value=1)\n", + " output = Output()\n", + "\n", + " def display_image(index):\n", + " with output:\n", + " output.clear_output(wait=True)\n", + " display(images[index])\n", + "\n", + " # Link the slider to the display function\n", + " interact(display_image, index=slider)\n", + "\n", + " # Display the widgets\n", + " # display(slider)\n", + " display(output)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config['mode'] = 'latent'\n", + "run(config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config['mode'] = 'rgb'\n", + "run(config)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..edd3510 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,54 @@ +# Changelog + +All notable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), +and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +### Types of changes + +- `Added` for new features. +- `Changed` for changes in existing functionality. +- `Deprecated` for soon-to-be removed features. +- `Removed` for now removed features. +- `Fixed` for any bug fixes. +- `Security` in case of vulnerabilities. + +## [Unreleased] + +### Added + +- A simple jupyter notebook (#55). +- `sdf_bias` as an alternative way for SDF initialization in `implicit-volume` (#57). +- Automatically remove outliers with a small number of faces when extracting surfaces (#61). +- The implementation of ProlificDreamer (#74, #105). +- An experimental implementation of using Zero-1-to-3 for 3D generation from a single image (#71). +- Support mesh initialization for `implicit-sdf` (#90). +- Easy-to-use geometry conversion by `system.geometry_convert_from`. This is used in the Magic3D and ProlificDreamer system and may inspire applications connecting multiple systems/algorithms (#105). +- Support prompt debiasing and manual assignment of view-dependent prompts (#98). +- The implementation of Perp-Neg (#98). +- Support patch-based renderer (#154). +- Support 3D reconstruction from multi-view images and 3D editing based on InstructNeRF2NeRF/ControlNet (#119). +- Support NeuS/VolSDF volume renderer and the coarse stage of TextMesh (#162,#121). +- Gradio web interface (#183). + +### Changed + +- Remove `trainer` from the constructor arguments of prompt processors (#56). +- Use a reparametrization trick for the SDS loss (#57). +- Make Magic3D coarse stage use analytic normal and orientation loss. +- Move the logic of getting text embeddings according to camera settings from prompt processors to guidance (#77). +- Remove `from_coarse` from the Magic3D system. Use `system.geometry_convert_from` instead (#105). + +### Fixed + +- Fix errors caused by empty rays (#152). + +## [v0.1.0] + +### Added + +- Implementation of DreamFusion, Magic3D, SJC, Latent-NeRF and Sketch-Shape. +- Implementation of the geometry stage of Fantasia3D. +- Multi-GPU training support (#33). +- Mesh export, supporting obj with mtl and obj with vertex colors (#44). diff --git a/DOCUMENTATION.md b/DOCUMENTATION.md new file mode 100644 index 0000000..e006754 --- /dev/null +++ b/DOCUMENTATION.md @@ -0,0 +1,493 @@ +## Common Configurations + +| name | type | description | +| ------------- | ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| name | str | Name of the experiment. Default: "default" | +| description | str | Description of the experiment. Default: "" | +| tag | str | Tag of the experiment. Default: "" | +| seed | str | Global seed of the experiment. Used by `seed_everything` of PyTorch-Lightning. Default: 0 | +| use_timestamp | bool | Whether to use the current timestamp as the suffix of the tag. Default: True | +| timestamp | Optional[str] | The timestamp as the suffix of the tag. DO NOT set this manually. Default: None | +| exp_root_dir | str | The root directory for outputs of all the experiments. Default: "outputs" | +| exp_dir | str | The directory for outputs of the current experiment. DO NOT set this manually. It will be automatically set to `[exp_root_dir]/[name]`. | +| trial_name | str | Name of the trial. DO NOT set this manually. It will be automatically set to `[tag]@[timestamp]`. | +| trial_dir | str | The directory for outputs for the current trial. DO NOT set this manually. It will be automatically set to `[exp_root_dir]/[name]/[trial_name].` | +| resume | Optional[str] | The path to the checkpoint file to resume from. Default: None | +| data_type | str | Type of the data module used. See [here](https://github.com/threestudio-project/threestudio/blob/main/DOCUMENTATION.md#data) for supported data modules. Default: "" | +| data | dict | Configurations of the data module. Default: {} | +| system_type | str | Type of the system used. See [here](https://github.com/threestudio-project/threestudio/blob/main/DOCUMENTATION.md#systems) for supported systems. Default: "" | +| system | dict | Configurations of the system. Defaut: {} | +| trainer | dict | Configurations of PyTorch-Lightning Trainer. See https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api for supported arguments. Exceptions: `logger` and `callbacks` are set in `launch.py`. Default: {} | +| checkpoint | dict | Configurations of PyTorch-Lightning ModelCheckpoint callback, which defines when the checkpoint will be saved. See https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.ModelCheckpoint.html#modelcheckpoint for supported arguments. Default: {} | + +## Data + +### random-camera-datamodule + +| name | type | description | +| ---------------------- | --------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| height | Union[int, List[int]] | Height of the rendered image in training, should be an integer or a list of integers. If a list of integers, the training height will change according to `resolution_milestones`. Default: 64 | +| width | Union[int, List[int]] | Width of the rendered image in training, should be an integer or a list of integers. If a list of integers, the training width will change according to `resolution_milestones`. Default: 64 | +| batch_size | Union[int, List[int]] | Number of images per batch in training. If a list of integers, the batch_size will change according to `resolution_milestones`. Default: 1 | +| resolution_milestones | List[int] | The steps where the training resolution will change, must be in ascending order and in the length of `len(height) - 1`. Default: [] | +| eval_height | int | Height of the rendered image in validation/testing. Default: 512 | +| eval_width | int | Width of the rendered image in validation/testing. Default: 512 | +| eval_batch_size | int | Number of images per batch in validation/testing. DO NOT change this. Default: 1 | +| elevation_range | Tuple[float,float] | Camera elevation angle range to sample from in training, in degrees. Default: (-10,90) | +| azimuth_range | Tuple[float,float] | Camera azimuth angle range to sample from in training, in degrees. Default: (-180,180) | +| camera_distance_range | Tuple[float,float] | Camera distance range to sample from in training. Default: (1,1.5) | +| fovy_range | Tuple[float,float] | Camera field of view (FoV) range along the y direction (vertical direction) to sample from in training, in degrees. Default: (40,70) | +| camera_perturb | float | Random perturbation ratio for the sampled camera positions in training. The sampled camera positions will be perturbed by `N(0,1) * camera_perturb`. Default: 0.1 | +| center_perturb | float | Random perturbation ratio for the look-at point of the cameras in training. The look-at point wil be `N(0,1) * center_perturb`. Default: 0.2 | +| up_perturb | float | Random pertubation ratio for the up direction of the cameras in training. The up direction will be `[0,0,1] + N(0,1) * up_perturb`. Default: 0.02 | +| light_position_perturb | float | Used to get random light directions from camera positions, only used when `light_sample_strategy="dreamfusion"`. The camera positions will be perturbed by `N(0,1) * light_position_perturb`, then the perturbed positions are used to determine the light directions. Default: 1.0 | +| light_distance_range | Tuple[float,float] | Point light distance range to sample from in training. Default: (0.8,1.5) | +| eval_elevation_deg | float | Camera elevation angle in validation/testing, in degrees. Default: 150 | +| eval_camera_distance | float | Camera distance in validation/testing. Default: 15 | +| eval_fovy_deg | float | Camera field of view (FoV) along the y direction (vertical direction) in validation/testing, in degrees. Default: 70 | +| light_sample_strategy | str | Strategy to sample point light positions in training, in ["dreamfusion", "magic3d"]. "dreamfusion" uses strategy described in the DreamFusion paper; "magic3d" uses strategy decribed in the Magic3D paper. Default: "dreamfusion" | +| batch_uniform_azimuth | bool | Whether to ensure the uniformity of sampled azimuth angles in training as described in the Fantasia3D paper. If True, the `azimuth_range` is equally divided into `batch_size` bins and the azimuth angles are sampled from every bins. Default: True | +| progressive_until | int | Number of iterations until which to progressively (linearly) increase elevation_range and azimuth_range from [`eval_elevation_deg`, `eval_elevation_deg`] and `[0.0, 0.0]`, to those values specified in `elevation_range` and `azimuth_range`. 0 means the range does not linearly increase. Default: 0 | + +## Systems + +Systems contain implementation of training/validation/testing logic for different methods. + +**Common configurations for systems** + +| name | type | description | +| ----------------------------- | ------------------- | ---------------------------------------------------------------------------------------------------------------------------- | +| loss | dict | Dict that contains loss-related configurations. Default: {} | +| optimizer | dict | Optimizer configurations. Default: {} | +| scheduler | Optional[dict] | Learning rate scheduler configurations. If None, does not use a scheduler. Default: None | +| weights | Optional[str] | Path to the weights to be loaded. This is different from `resume` in that this does not resume training state. Default: None | +| weights_ignore_modules | Optional[List[str]] | List of modules that should be ignored when loading weights. Default: None | +| cleanup_after_validation_step | bool | Whether to empty cache after each validation step. This will slow down validation. Default: False | +| cleanup_after_test_step | bool | Whether to empty cache after each test step. This will slow down testing. Default: False | + +Currently all implemented systems inherit to `BaseLift3DSystem`, which has the following common configurations: + +| name | type | description | +| -------------------------------- | ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| geometry_type | str | Type of the geometry used in the system. See [here](https://github.com/threestudio-project/threestudio/blob/main/DOCUMENTATION.md#geometry) for supported geometry. | +| geometry | dict | Configurations of the geometry. | +| geometry_convert_from | Optional[str] | The path to a checkpoint from which the geometry is converted. If not None, initialize the geometry from the specified source model. Default: None | +| geometry_convert_override | dict | Configurations to override when initializing from a source geometry, only used when `geometry_convert_from` is specified. A typical use case is to specify an isosurface threshold value. Default: {} | +| geometry_convert_inherit_texture | bool | Whether to load the encoding and feature network from the source geometry during conversion, only used when `geometry_convert_from` is specified. Default: False | +| material_type | str | Type of the material used in the system. See [here](https://github.com/threestudio-project/threestudio/blob/main/DOCUMENTATION.md#material) for supported materials. | +| matrial | dict | Configurations of the material. | +| background_type | str | Type of the background used in the system. See [here](https://github.com/threestudio-project/threestudio/blob/main/DOCUMENTATION.md#background) for supported background. | +| background | dict | Configurations of the background. | +| renderer_type | str | Type of the renderer used in the system. See [here](https://github.com/threestudio-project/threestudio/blob/main/DOCUMENTATION.md#renderers) for supported renderers. | +| renderer | dict | Configurations of the renderer. | +| guidance_type | str | Type of the guidance used in the system. See [here](https://github.com/threestudio-project/threestudio/blob/main/DOCUMENTATION.md#guidance) for supported guidance. | +| guidance | dict | Configurations of the guidance. | +| prompt_processor_type | str | Type of the prompt processor used in the system. See [here](https://github.com/threestudio-project/threestudio/blob/main/DOCUMENTATION.md#prompt-processors) for supported prompt processors. | +| prompt_processor | dict | Configurations of the prompt processor. | +| exporter_type | str | Type of the exporter used in the system. Only used in export stage. See [here](https://github.com/threestudio-project/threestudio/blob/main/DOCUMENTATION.md#prompt-processors) for supported exporters. Default: "mesh-exporter" | +| exporter | dict | Configurations of the exporter. | + +### dreamfusion-system + +This system has all the common configurations. + +### magic3d-system + +This system has all the common configurations, along with the following unique configurations: + +| name | type | description | +| ---------- | ---- | --------------------------------------------------------------------------------- | +| refinement | bool | Whether to perform refinement (second stage in the Magic3D paper). Default: False | + +### sjc-system + +This system has all the common configurations, along with the following unique configurations: +| name | type | description | +| ------------------ | ---- | ------------------------------------------------------------------------------------------------------------------------------------------- | +| subpixel_rendering | bool | Whether to perform subpixel rendering in validation/testing, which decodes a `128x128` latent feature map instead of `64x64`. Default: True | + +### latentnerf-system + +This system has all the common configurations, along with the following unique configurations: + +| name | type | description | +| ----------- | ------------- | -------------------------------------------------------------------------------- | +| refinement | bool | Whether to perform RGB space refinement. Default: False | +| guide_shape | Optional[str] | Path to the .obj file as the shape guidance, used in Sketch-Shape. Default: None | + +### fantasia3d-system + +This system has all the common configurations, along with the following unique configurations: + +| name | type | description | +| ------------ | ---- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| latent_steps | int | Number of steps for geometry optimization in latent space. In the first `latent_steps` steps, low resolution normal and mask are concatenated and fed to the latent diffusion model. After this high resolution normal is used to perform RGB space optimziation. Details are described in the Fantasia3D paper. Default: 2500 | +| texture | bool | Whether to perform texture training. Default: False | + +### prolificdreamer-system + +This system has all the common configurations, along with the following unique configurations: + +| name | type | description | +| ----------------- | ---- | ------------------------------------------------------------------------------------------------------ | +| stage | str | The training stage, in ["coarse", "geometry", "texture"]. Default: "coarse" | +| visualize_samples | bool | Whether to visualize samples of the pretrained and LoRA diffusion models in validation. Default: False | + +## Geometry + +Geometry models properties for locations in space, including density, SDF, feature and normal. + +**Common configurations for implicit geometry** + +| name | type | description | +| ------------------------------------ | ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| radius | float | Half side length of the scene bounding box. Default: 1.0 | +| isosurface | bool | Whether to enable surface extraction. Default: True | +| isosusrface_method | str | Method for surface extraction, in ["mc", "mt"]. "mc" uses the marching cubes algorithm, not differentiable; "mt" uses the marching tetrahedra algorithm, differentiable. Default: "mt" | +| isosurface_resolution | int | Grid resolution for surface extraction. Default: 128 | +| isosurface_threshold | Union[float,str] | The threshold value to determine the surface location of the implicit field, in [float, "auto"]. If "auto", use the mean value of the field as the threshold. Default: 0 | +| isosurface_chunk | int | Chunk size when computing the field value on grid vertices, used to prevent OOM. If 0, does not use chunking. Default: 0 | +| isosurface_coarse_to_fine | bool | Whether to extract the surface in a coarse-to-fine manner. If True, will first extract a coarse surface to get a tight bounding box, which is then used to extract a fine surface. Default: True | +| isosurface_deformable_grid | bool | Whether to optimize positions of grid vertices for surface extraction. Only support `isosurface_method=mt`. Default: False | +| isosurface_remove_outliers | bool | Whether to remove outlier components according to the number of faces. Only remove if the isosurface process does not require gradient. Default: True | +| isosurface_outlier_n_faces_threshold | Union[int, float] | Extracted mesh components with number of faces less than this threshold will be removed if `isosurface_remove_outliers=True`. If `int`, direcly used as the threshold number of faces; if `float`, used as the ratio of all face numbers to compute the threshold. Default: 0.01 | + +### implicit-volume + +| name | type | description | +| ---------------------------- | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| n_input_dims | int | Number of input dimensions. Default: 3 (xyz) | +| n_feature_dims | int | Number of dimensions for the output features. Note that this should be aligned with the material used. Default: 3 (albedo) | +| density_activation | str | Density activation function. See `get_activation` in `utils/ops.py` for all supported activation functions. Default: "softplus" | +| density_bias | Union[float,str] | Offset value to be added to the pre-activated density, in [float, "blob_dreamfusion", "blob_magic3d"]. If "blob_dreamfusion", uses the blob density bias proposed in DreamFusion; if "blob_magic3d", uses the blob density bias proposed in Magic3D. Default: "blob_magic3d" | +| density_blob_scale | float | Controls the magnitude of the blob density if `density_bias` in ["blob_dreamfusion", "blob_magic3d"]. Default: 10 | +| density_blob_std | float | Controls the divergence of the blob density if `density_bias` in ["blob_dreamfusion", "blob_magic3d"]. Default: 0.5 | +| pos_encoding_config | dict | Configurations for the positional encoding. See https://github.com/NVlabs/tiny-cuda-nn/blob/master/DOCUMENTATION.md#encodings for supported arguments. Default: {} | +| mlp_network_config | dict | Configurations for the MLP head for geometry attribute prediction (density, feature ...). See https://github.com/NVlabs/tiny-cuda-nn/blob/master/DOCUMENTATION.md#networks for supported arguments. Default: {} | +| normal_type | str | How the normal is computed, in ["analytic", "finite_difference", "pred"]. If "analytic", uses PyTorch auto-differentiation to compute the analytic normal; if "finite_difference", uses finite difference to compute the approximate normal; if "pred", uses an MLP network to predict the normal. Default: "finite_difference" | +| finite_difference_normal_eps | float | The small epsilon value in finite difference to estimate the normal, used when `normal_type="finite_difference"`. Default: 0.01 | +| isosurface_threshold | Union[float,str] | Inherit from common configurations, but default to "auto". Default: "auto" | + +### implicit-sdf + +| name | type | description | +| ---------------------------- | ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| n_input_dims | int | Number of input dimensions. Default: 3 (xyz) | +| n_feature_dims | int | Number of dimensions for the output features. Note that this should be aligned with the material used. Default: 3 (albedo) | +| pos_encoding_config | dict | Configurations for the positional encoding. See https://github.com/NVlabs/tiny-cuda-nn/blob/master/DOCUMENTATION.md#encodings for supported arguments. Default: {} | +| mlp_network_config | dict | Configurations for the MLP head for geometry attribute prediction (sdf, feature ...). See https://github.com/NVlabs/tiny-cuda-nn/blob/master/DOCUMENTATION.md#networks for supported arguments. Default: {} | +| normal_type | str | How the normal is computed, in ["finite_difference", "pred"]. If "finite_difference", uses finite difference to compute the approximate normal; if "pred", uses an MLP network to predict the normal. Default: "finite_difference" | +| finite_difference_normal_eps | float | The small epsilon value in finite difference to estimate the normal, used when `normal_type="finite_difference"`. Default: 0.01 | +| shape_init | Optional[str] | The shape to initializa the SDF as, in [None, "sphere", "ellipsoid"]. If None, does not initialize; if "sphere", initialized as a sphere; if "ellipsoid", initialized as an ellipsoid. Default: None | +| shape_init_params | Optional[Any] | Parameters to specify the SDF initialization. If `shape_init="sphere"`, a float is used for the sphere radius; if `shape_init="ellipsoid"`, a tuple of three floats is used for the radius along x/y/z axis. Default: None | +| force_shape_init | bool | Whether to force initialization of the SDf even if weights are provided. Default:False | +| sdf_bias | Optional[float,str] | Bias value to be added to the network output SDF, in [float, "sphere", "ellipsoid"]. If "sphere", the SDF of a sphere is added; if "ellipsoid", the pseudo SDF of an ellipsoid is added. This can be used for SDF initialization as an alternative to `shape_init`. Default: 0.0 | +| sdf_bias_params | Optional[Any] | Parameters to specify the SDF initialization based on `sdf_bias`. If `sdf_bias="sphere"`, a float is used for the sphere radius; if `sdf_bias="ellipsoid"`, a tuple of three floats is used for the radius along x/y/z axis. Default: None | + +### volume-grid + +An explicit geometry parameterized with a feature volume. The feature volume has a shape of `(n_feature_dims + 1) x grid_size`, one channel for density and the rest for material. The density is first scaled, then biased and finally activated. + +| name | type | description | +| -------------------- | -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| grid_size | tuple[int, int, int] | The resolution of the feature volume. Default: (100, 100, 100) | +| n_feature_dims | int | The feature dimensions for its material. Default: 3 | +| density_activation | Optional[str] | The activation to get the density value. Default: "softplus" | +| density_bias | Union[float, str] | The initialization of the density. A float value indicates uniform initialization and `blob` indicates a ball centered at the center. Default: "blob" | +| density_blob_scale | float | The parameter for blob initialization. Default: 5.0 | +| density_blob_std | float | The parameter for blob initialization. Default: 0.5 | +| normal_type | Optional[str] | The way to compute the normal from density. If set to "pred", the normal is produced with another volume in the shape of `3 x grid_size`. Default: "finite_difference" | +| isosurface_threshold | Union[float,str] | Inherit from common configurations, but default to "auto". Default: "auto" | + +**Common configurations for explicit geometry** + +| name | type | description | +| ------------------- | ---- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| pos_encoding_config | dict | Configurations for the positional encoding. See https://github.com/NVlabs/tiny-cuda-nn/blob/master/DOCUMENTATION.md#encodings for supported arguments. Default: {} | +| mlp_network_config | dict | Configurations for the MLP head for feature prediction. See https://github.com/NVlabs/tiny-cuda-nn/blob/master/DOCUMENTATION.md#networks for supported arguments. Default: {} | +### tetrahedra-sdf-grid + +| name | type | description | +| ------------------------------------ | ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| isosurface_resolution | int | Tetrahedra grid resolution for surface extraction. Default: 128 | +| isosurface_deformable_grid | bool | Whether to optimize positions of tetrahedra grid vertices for surface extraction. Default: True | +| isosurface_remove_outliers | bool | Whether to remove outlier components according to the number of faces. Only remove if the isosurface process does not require gradient. Default: False | +| isosurface_outlier_n_faces_threshold | Union[int, float] | Extracted mesh components with number of faces less than this threshold will be removed if `isosurface_remove_outliers=True`. If `int`, direcly used as the threshold number of faces; if `float`, used as the ratio of all face numbers to compute the threshold. Default: 0.01 | +| geometry_only | bool | Whether to only model the SDF. If True, the feature prediction is ommited. Default:False | +| fix_geometry | bool | Whether to optimize the geometry. If True, the SDF (and grid vertices if `isosurface_deformable_grid=True`) is fixed. Default: False | + +### Custom mesh + +| shape_init | str | The shape to initializa the SDF as. Should be formatted as "mesh:path", where `path` points to the custom mesh. Default: "" | +| shape_init_params | Optional[Any] | Parameters to specify the SDF initialization. A single float is used for uniform scaling; a tuple of three floats is used for scalings along x/y/z axis. Default: None | + +## Material + +The material module outputs colors or color latents conditioned on the sampled positions, view directions, and sometimes light directions and normals. + +### neural-radiance-material + +A material with view dependent effects, parameterized with a network(MLP), similar with that in NeRF. + +| name | type | description | +| ------------------- | ---- | ----------------------------------------------------------------------------------------------------------------------------- | +| input_feature_dims | int | The dimensions of the input feature. Default: 8 | +| color_activation | str | The activation mapping the network output to the color. Default: "sigmoid" | +| dir_encoding_config | dict | The config of the positional encoding applied on the ray direction. Default: {"otype": "SphericalHarmonics", "degree": 3} | +| mlp_network_config | dict | The config of the MLP network. Default: { "otype": "VanillaMLP", "activation": "ReLU", "n_neurons": 16, "n_hidden_layers": 2} | + +### pbr-material + +A physically-based rendering (PBR) material. +Currently we support learning albedo, metallic, and roughness. (normal is not supported currently.) + +| name | type | description | +| ------------------- | ----- | ---------------------------------------------------------------------------------------------------------------- | +| material_activation | str | The activation mapping the network output to the materials (albedo, metallic, and roughness). Default: "sigmoid" | +| environment_texture | str | Path to the environment light map file (`*.hdr`). Default: "load/lights/aerodynamics_workshop_2k.hdr" | +| environment_scale | float | Scale of the environment light pixel values. Default: 2.0 | +| min_metallic | float | Minimum value for metallic. Default: 0.0 | +| max_metallic | float | Maximum value for metallic. Default: 0.9 | +| min_roughness | float | Minimum value for roughness. Default: 0.08 | +| max_roughness | float | Maximum value for roughness. Default: 0.9 | +| use_bump | bool | Whether to train with tangent-space normal perturbation. Default: True | + +### no-material + +A material without view dependet effects, just map features to colors. + +| name | type | description | +| ------------------ | -------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| n_output_dims | int | The dimensions of the material color, e.g. 3 for RGB and 4 for latent. Default: 3 | +| color_activation | str | The activation mapping the network output or the feature to the color. Default: "sigmoid" | +| mlp_network_config | Optional[dict] | The config of the MLP network. Set to `None` to directly map the input feature to the color with `color_activation`, otherwise the feature first goes through an MLP. Default: None | +| input_feature_dims | Optional[int] | The dimensions of the input feature. Required when use an MLP. Default: None | + +### diffuse-with-point-light-material + +| name | type | description | +| ------------------- | ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| ambient_light_color | Tuple[float,float,float] | The ambient light color for lambertian shading, used when `soft_shading=False`. Default: (0.1,0.1,0.1) | +| diffuse_light_color | Tuple[float,float,float] | The diffuse light color for lambertian shading, used when `soft_shading=False`. Default: (0.9,0.9,0.9) | +| ambient_only_steps | int | Number of steps that use albedo color as input to the guidance. Default: 1000 | +| diffuse_prob | float | Use shaded color with a probability of `diffuse_prob` and albedo color with a probability of `1-diffuse_prob` after `ambient_only_steps`. Default: 0.75 | +| textureless_prob | float | Use textureless shaded color with a probability of `textureless_prob` and lambertian shaded color with a probability of `1-textureless_prob`when using shaded color. Default: 0.5 | +| albedo_activation | str | Activation function for the albedo color. Default: "sigmoid" | +| soft_shading | bool | If True, uses a soft version of lambertian shading in training, which randomly samples the ambient light color and diffuse light color. Proposed in the Magic3D paper. Default: False | + +### sd-latent-adapter-material + +No specific configuration. + +## Background + +The background should output colors or color latents conditioned on the ray directions. + +**Common configurations for background** + +| name | type | description | +| ------------- | ---- | ---------------------------------------------------------------------------------- | +| n_output_dims | int | The dimension of the background color, e.g. 3 for RGB and 4 for latent. Default: 3 | + +### solid-color-background + +A background with a solid color. + +| name | type | description | +| ------- | ----- | ------------------------------------------------------------------------------------------------------------------------ | +| color | tuple | The initialized color of the background with each value in [0,1], should match `n_output_dims`. Default: (1.0, 1.0, 1.0) | +| learned | bool | Whether to optimize the background. Default: True | + +### textured-background + +A background with colors parameterized with a texture map. + +| name | type | description | +| ---------------- | ---- | --------------------------------------------------------------------------- | +| height | int | The height of the texture map. Default: 64 | +| width | int | The width of the texture map. Default: 64 | +| color_activation | str | The activation mapping the texture feature to the color. Default: "sigmoid" | + +### neural-environment-map-background + +A background parameterized with a neural network (MLP). + +| name | type | description | +| ------------------- | ---------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- | +| color_activation | str | The activation mapping the network output to the color. Default: "sigmoid" | +| dir_encoding_config | dict | The config of the positional encoding applied on the ray direction. Default: {"otype": "SphericalHarmonics", "degree": 3} | +| mlp_network_config | dict | The config of the MLP network. Default: { "otype": "VanillaMLP", "activation": "ReLU", "n_neurons": 16, "n_hidden_layers": 2} | +| random_aug | bool | Whether to use random color augmentation. May be able to improve the correctness of the model. Default: False | +| random_aug_prob | float | The probability to use random color augmentation. Default: 0.5. | +| eval_color | Optional[Tuple[float,float,float]] | The color used in validation/testing. Default: None | + +## Renderers + +Renderers takes geometry, material, and background to produce images given camera and light specifications. + +**Common configurations for renderers** + +| name | type | description | +| ------ | ----- | --------------------------------------------------------------------------------------------------------------------------- | +| radius | float | Half side length of the scene bounding box. This should be the same as `radius` of the geometry in most cases. Default: 1.0 | + +### nerf-volume-renderer + +| name | type | description | +| ----------------------------------------------------------------- | ----- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| num_samples_per_ray | float | Number of sample points along each ray. Default: 1.0 | +| randomized | bool | Whether to randomly perturb the sample points in training. Default: True | +| eval_chunk_size | int | Number of sample points per chunk in validation/testing, to prevent OOM. Default: 160000 | +| estimator | str | The type of sampling estimator. Shoule be one of [occgrid, proposal, importance]. Default: occgrid. | +| grid_prune (applicable when using occgrid) | bool | Whether to maintain an occupancy grid and prune sample points in empty space using NeRFAcc. Default: True | +| prune_alpha_threshold (applicable when using occgrid) | bool | Whether to prune sample points with lower density, only effective when `grid_prune=true`. Default: True | +| proposal_network_config (applicable when using proposal) | dict | The proposal network configuration, used for density estimation. Default: None | +| prop_optimizer_config (applicable when using proposal) | dict | The optimizer configuration for the proposal network. Note that the renderer is not a part of the system's trainable parameters. So the optimizer should be manually specified here, and the optimization is take by Nerfacc. | +| prop_scheduler_config (applicable when using proposal) | dict | The learning scheduler for the above optimizer. Default: None | +| num_samples_per_ray_proposal (applicable when using proposal) | int | Number of sample points along each ray for proposal network. Will sample `num_samples_per_ray` points according to the proposal sampling. Default: 64 | +| num_samples_per_ray_importance (applicable when using importance) | int | Number of sample points in NeRF coarse sampling and `num_samples_per_ray` is for fine sampling Default: 64 | + + + +### neus-volume-renderer + +| name | type | description | +| ----------------------------------------------------------------- | ----- | ---------------------------------------------------------------------------------------------------------- | +| num_samples_per_ray | float | Number of sample points along each ray. Default: 1.0 | +| randomized | bool | Whether to randomly perturb the sample points in training. Default: True | +| eval_chunk_size | int | Number of sample points per chunk in validation/testing, to prevent OOM. Default: 160000 | +| estimator | str | The type of sampling estimator. Shoule be one of [occgrid, importance]. Default: occgrid. | +| grid_prune (applicable when using occgrid) | bool | Whether to maintain an occupancy grid and prune sample points in empty space using NeRFAcc. Default: True | +| prune_alpha_threshold (applicable when using occgrid) | bool | Whether to prune sample points with lower density, only effective when `grid_prune=true`. Default: True | +| num_samples_per_ray_importance (applicable when using importance) | int | Number of sample points in NeRF coarse sampling and `num_samples_per_ray` is for fine sampling Default: 64 | +| learned_variance_init | float | Initialized value for the learned surface variance. Default: 0.3 | +| cos_anneal_end_steps | int | End steps for the linear cosine annealing technique proposed in the NeuS paper. Default: 0 | +| use_volsdf | bool | Whether to use the VolSDF formulation for SDF-to-alpha conversion. Default: False | +| near_plane | float | Distance from camera to the near plane. Default: 0.0 | +| far_plane | float | Distance from camera to the far plane. Default: 1e10 | + +### nvdiff-rasterizer + +| name | type | description | +| ------------ | ---- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| context_type | str | Rasterization context type used by nvdiffrast, in ["gl", "cuda"]. See the [nvdiffrast documentation](https://nvlabs.github.io/nvdiffrast/#rasterizing-with-cuda-vs-opengl-new) for more details. | + +### patch-renderer + +The patch-renderer first renders a full low-resolution downsampled image and then randomly renders a local patch at the original resolution level, which can significantly reduce memory usage during high-resolution training. +| name | type | description | +| ------------------ | --------------------- | ----------------------------------------------------------------------- | +| patch_size | int | The size of the local patch. Default: 128 | +| global_downsample | int | Downsample scale of the original rendering size. Default: 4 | +| global_detach | bool | Whether to detach the gradient of the downsampled image. Default: False | +| base_renderer_type | str | The type of base renderer. | +| base_renderer | VolumeRenderer.Config | The configuration of the base renderer. | + +## Guidance + +Given an image or its latent input, the guide should provide its gradient conditioned on a text input so that the image can be optimized with gradient descent to better match the text. + +**Common configurations for guidance** + +| name | type | description | +| --------------------------------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| enable_memory_efficient_attention | bool | Whether to enable memory efficient attention in xformers. This will lead to lower GPU memory usage and a potential speed up at inference. Speed up at training time is not guaranteed. Default: false | +| enable_sequential_cpu_offload | bool | Whether to offload all models to CPU. This will use `accelerate`, significantly reducing memory usage but slower. Default: False | +| enable_attention_slicing | bool | Whether to use sliced attention computation. This will save some memory in exchange for a small speed decrease. Default: False | +| enable_channels_last_format | bool | Whether to use Channels Last format for the unet. Default: False (Stable Diffusion) / True (DeepFloyd) | +| pretrained_model_name_or_path | str | The pretrained model path in huggingface. Default: "runwayml/stable-diffusion-v1-5" (for `stable-diffusion-guidance`) / "DeepFloyd/IF-I-XL-v1.0" (for `deep-floyd-guidance`) / "stabilityai/stable-diffusion-2-1-base" (for `stable-diffusion-vsd-guidance`) | +| guidance_scale | float | The classifier free guidance scale. Default: 100.0 (for `stable-diffusion-guidance`) / 20.0 (for `deep-floyd-guidance`) | +| grad_clip | Optional[Any] | The gradient clip value. None or float or a list in the form of [start_step, start_value, end_value, end_step]. Default: None | +| half_precision_weights | bool | Whether to use float16 for the diffusion model. Default: True | +| min_step_percent | float | The precent range (min value) of the random timesteps to add noise and denoise. Default: 0.02 | +| max_step_percent | float | The precent range (max value) of the random timesteps to add noise and denoise. Default: 0.98 | +| weighting_strategy | str | The choice of w(t) of the sds loss, in ["sds", "uniform", "fantasia3d"]. Default: "sds" | +| view_dependent_prompting | bool | Whether to use view dependent prompt, i.e. add front/side/back/overhead view to the original prompt. Default: True | + +For the first three options, you can check more details in [pipe_stable_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py) and [pipeline_if.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py) in diffusers. + +### stable-diffusion-guidance + +| name | type | description | +| ------------------------- | -------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- | +| use_sjc | bool | Whether to use score jacobian chaining (SJC) instead of SDS. Default: False | +| var_red | bool | Whether to use Eq. 16 in [SJC paper](https://arxiv.org/pdf/2212.00774.pdf). Default: True | +| token_merging | bool | Whether to use token merging. This will speed up the unet forward and slightly affect the performance. Default: False | +| token_merging_params | Optional[dict] | The config for token merging. See [here](https://github.com/dbolya/tomesd/blob/main/tomesd/patch.py#L183-L213) for supported arguments. Default: {} | +| anneal_start_step | Optional[int] | If specified, denotes at which step to perform t annealing. Default: None | + +### deep-floyd-guidance + +No specific configuration. + +## stable-diffusion-vsd-guidance + +| name | type | description | +| ---------------------------------- | ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| pretrained_model_name_or_path_lora | str | The pretrained base model path for the LoRA model. Default: "stabilityai/stable-diffusion-2-1" | +| guidance_scale_lora | float | The classifier free guidance scale for the LoRA model. Default: 1. | +| lora_cfg_training | bool | Whether to adopt classifier free guidance training strategy in LoRA training. If True, will zero out the camera condition with a probability 0.1. Default: True | +| camera_condition_type | str | Which to use as the camera condition for the LoRA model, in ["extrinsics", "mvp"]. Default: "extrinsics" | + +## Prompt Processors + +Prompt processors take a user prompt and compute text embeddings for training. The type of the prompt processor should match that of the guidance. + +**Common configurations for prompt processors** + +| name | type | description | +| ---------------------------------------------- | ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| prompt | str | The text prompt. Default: "a hamburger" | +| prompt_front | str | Manually assigned prompt for the front view. If `None`, use the same as `prompt`. Default: None | +| prompt_side | str | Manually assigned prompt for the side view. If `None`, use the same as `prompt`. Default: None | +| prompt_back | str | Manually assigned prompt for the back view. If `None`, use the same as `prompt`. Default: None | +| prompt_overhead | str | Manually assigned prompt for the overhead view. If `None`, use the same as `prompt`. Default: None | +| negative_prompt | str | The uncondition text input in Classifier Free Guidance. Default: "" | +| pretrained_model_name_or_path | str | The pretrained model path in huggingface. Default: "runwayml/stable-diffusion-v1-5" (for `stable-diffusion-prompt-processor`) / "DeepFloyd/IF-I-XL-v1.0" (fpr `deep-floyd-prompt-processor`) | +| overhead_threshold | float | Consider the view as overhead when the elevation degree > overhead_threshold. Default: 60.0 | +| front_threshold | float | Consider the view as front when the azimuth degree in [-front_threshold, front_threshold]. Default: 45.0 | +| back_threshold | float | Consider the view as back when the azimuth degree > 180 - back_threshold or < -180 + back_threshold. Default: 45.0 | +| view_dependent_prompt_front | bool | Whether to put the vide dependent prompt in front of the original prompt. If set to True, the final prompt will be `a front/back/side/overhead view of [prompt]`, otherwise it will be `[prompt], front/back/side/overhead view`. Default: False | +| use_cache | bool | Whether to cache computed text embeddings. If True, will use cached text embeddings if available. Default: True | +| spawn | bool | Whether to spawn a new process to compute text embeddings. Must set to True if using multiple GPUs and DeepFloyd-IF guidance. Default: True | +| use_perp_neg | bool | Whether to use the Perp-Neg algorithm to alleviate the multi-face problem. Default: False | +| perp_neg_f_sb | Tuple[float,float,float] | | +| perp_neg_f_fsb | Tuple[float,float,float] | | +| perp_neg_f_fs | Tuple[float,float,float] | | +| perp_neg_f_sf | Tuple[float,float,float] | | +| use_prompt_debiasing | bool | Whether to use the prompt debiasing algorithm to compute debiased view-dependent prompts. Default: False | +| pretrained_model_name_or_path_prompt_debiasing | str | The pretrained model path for prompt debiasing. Default: "bert-base-uncased" | +| prompt_debiasing_mask_ids | Optional[List[int]] | Index of words that can potentially be removed in prompt debiasing. If `None`, all words can be removed. Default: None | + +### stable-diffusion-prompt-processor + +No specific configuration. + +### deep-floyd-prompt-processor + +No specific configuration. + +## Exporters + +Exporters output assets like textured meshes, which can be used for further processing. + +**Common configurations for exporters** + +| name | type | description | +| ---------- | ---- | ------------------------------------------- | +| save_video | bool | Whether to save a 360 video. default: False | + +### mesh-exporter + +| name | type | description | +| -------------------- | ---- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| fmt | str | The format to save, in ["obj-mtl", "obj"]. If "obj-mtl", save to an obj file with mtl material specification; if "obj", save to an obj file with vertex colors. Default: "obj-mtl" | +| save_name | str | Filename of the saved mesh model, without extension. Default: "model" | +| save_normal | bool | Whether to save vertex normal. Default: False | +| save_uv | bool | Whether to save texture coordinates. If True, will use xatlas to perform UV unwrapping. Default: True | +| save_texture | bool | Whether to save texture information. If True, will save texture maps if `fmt="obj-mtl"`, and will save vertex colors if `fmt="obj"`. Note that `save_uv` must be True for `save_texture=True` and `fmt="obj-mtl"`. Default: True | +| texture_size | int | Texture map size, used when `save_texture=True` and `fmt="obj-mtl"`. Default: 1024 | +| texture_format | str | Texture map file format, used when `save_texture=True` and `fmt="obj-mtl"`. Default: "jpg" | +| xatlas_chart_options | dict | Chart options for xatlas UV unwrapping, used when `save_uv=True`. See [here](https://github.com/MozillaReality/xatlas-web/blob/master/xatlas.h#L169) for supported options. Default: {} | +| xatlas_pack_options | dict | Pack options for xatlas UV unwrapping, used when `save_uv=True`. See [here](https://github.com/MozillaReality/xatlas-web/blob/master/xatlas.h#L201) for supported options. Default: {} | +| context_type | str | Rasterization context type used by nvdiffrast, in ["gl", "cuda"], used when `save_texture=True` and `fmt="obj-mtl"`. See the [nvdiffrast documentation](https://nvlabs.github.io/nvdiffrast/#rasterizing-with-cuda-vs-opengl-new) for more details. Default: "gl" | diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..261eeb9 --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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DreamMesh4D: Video-to-4D Generation with Sparse-Controlled Gaussian-Mesh Hybrid Representation

+ +

NeurIPS 2024

+ +
+ +[![arXiv](https://img.shields.io/badge/ArXiv-2410.06756-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2410.06756) +[![Project page](https://img.shields.io/badge/Project-Page-brightgreen)](https://lizhiqi49.github.io/DreamMesh4D/) + +
+ + +

+ +

+ +This as an official implementation of our NeurIPS 2024 paper [DreamMesh4D: Video-to-4D Generation with Sparse-Controlled Gaussian-Mesh Hybrid Representation](https://arxiv.org/abs/2410.06756), based on the [threestudio](https://github.com/threestudio-project/threestudio) framework. + + +## 🔧Installation +DreamMesh4D is implemented based on [threestudio](https://github.com/threestudio-project/threestudio). We modify the code of threestudio base repository to support fp16 inference for Zero123, and the main part of our method is under `custom/threestudio-dreammesh4d`. + +* Install `PyTorch >= 1.12`. We test our code on `torch2.2.1+cu118`, but other versions should also work fine. +``` +# torch2.2.1+cu118 +pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cu118 +``` +* (Optional, Recommended) Install ninja to speed up the compilation of CUDA extensions: +``` +pip install ninja +``` +* Install dependencies: +``` +# install 3D Gaussian modules +git clone --recursive https://github.com/ashawkey/diff-gaussian-rasterization +git clone https://github.com/DSaurus/simple-knn.git +# install +pip install -r requirements.txt +``` + + +## 🚀Quick Start + +### 1. Prepare the data +We follow the same data structure as [Consistent4D](https://github.com/yanqinJiang/Consistent4D). You can download the test dataset [here](https://drive.google.com/file/d/1jn18kA2FfKMnyQ6fisIn8rhBI0dr3NFk/view). + +If you'd like to use your own data, please preprocess it as follows: + +1. Split the video into individual frames and name each frame using the format `{id}.png`. +2. Segment the foreground object in each frame. (We use [rembg](https://github.com/danielgatis/rembg) for background removal.) + +Your input data should follow this structure: +``` +-image_seq_name + - 0.png + - 1.png + - 2.png + ... +``` + +### 2. Training + +#### 2.1 Static stage +For static 3D model generation and mesh export, we use Stable Zero123 in sds manner. You can follow the instructions provided in [threestudio](https://github.com/threestudio-project/threestudio) to generate 3D objects and export the coarse mesh. The example command: +``` +# 3d model generation with stable zero123 +python launch.py --config configs/stable-zero123.yaml --train data.image_path=path/to/ref/img + +# mesh export +python launch.py --config path/to/trial/dir/configs/parsed.yaml --export resume=path/to/trial/dir/ckpts/last.ckpt system.exporter_type=mesh-exporter system.exporter.fmt=obj system.geometry.isosurface_method=mc-cpu system.geometry.isosurface_resolution=256 +``` +**Note**: +* The argument `system.exporter.fmt=obj` is required, as we need vertex colors for initializing the colors of the Gaussians. +* Since marching-cubes always produces very dense faces, we suggest simplifying the exported mesh with following command to avoid too large computation overhead and "CUDA-out-of-memory" problem: + ``` + python custom/threestudio-dreammesh4d/scripts/mesh_simplification.py --mesh_path path/to/export/mesh --scale simplify_scale --output path/to/output/dir + ``` + +After getting coarse mesh, we attach the Gaussians and refine it: +``` +python launch.py --config custom/threestudio-dreammesh4d/configs/sugar_static_refine.yaml --train data.image_path=path/to/ref/img system.geometry.surface_mesh_to_bind_path=path/to/coarse/mesh +``` + + +#### 2.2 Dynamic stage +Run dynamic stage: +``` +python launch.py --config custom/threestudio-dreammesh4d/configs/sugar_dynamic_dg.yaml --train data.image_path=path/to/ref/img data.video_frames_dir=path/to/video system.data.video_length=video_frame_num system.geometry.surface_mesh_to_bind_path=path/to/refine/mesh system.weights=path/to/trial/dir/ckpts/last.ckpt +``` + +Then the deformed mesh under each timestamp can be exported with: +``` +python launch.py --config path/to/dynamic/dir/configs/parsed.yaml --export resume=path/to/dynamic/dir/ckpts/last.ckpt +``` + + + +## 📋News +- **[2024/10/09]** Code will be released soon! + + +## Credits +This project is built upon the awesome project [threestudio](https://github.com/threestudio-project) and thanks to the open-source of these works: [3D Gaussian Splatting](https://github.com/graphdeco-inria/gaussian-splatting) and [SuGaR](https://github.com/Anttwo/SuGaR). + + + +## 📌Citation +If you find our paper and code useful in your research, please consider giving a star and citation. + +``` +@inproceedings{li2024dreammesh4d, + title={DreamMesh4D: Video-to-4D Generation with Sparse-Controlled Gaussian-Mesh Hybrid Representation}, + author={Zhiqi Li and Yiming Chen and Peidong Liu}, + booktitle={Advances in Neural Information Processing Systems (NeurIPS)}, + year={2024} +} +``` diff --git a/configs/control4d-static.yaml b/configs/control4d-static.yaml new file mode 100644 index 0000000..572127d --- /dev/null +++ b/configs/control4d-static.yaml @@ -0,0 +1,118 @@ +name: "control4d-static" +tag: "${basename:${data.dataroot}}_${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "multiview-camera-datamodule" +data: + train_downsample_resolution: 2 + eval_downsample_resolution: 2 + dataroot: ??? + +system_type: "control4d-multiview-system" +system: + start_editing_step: 2000 + + geometry_type: "implicit-volume" + geometry: + radius: 2. + n_feature_dims: 11 + normal_type: analytic + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.4472692374403782 # max resolution 4096 + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + isosurface_resolution: 128 + isosurface_threshold: auto + isosurface_coarse_to_fine: true + + material_type: "hybrid-rgb-latent-material" + material: + n_output_dims: 11 + requires_normal: true + + background_type: "solid-color-background" + background: + n_output_dims: 11 + color: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + + renderer_type: "gan-volume-renderer" + renderer: + base_renderer_type: "nerf-volume-renderer" + base_renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + + guidance_type: "stable-diffusion-controlnet-guidance" + guidance: + control_type: "normal" + min_step_percent: 0.05 + max_step_percent: 0.8 + condition_scale: 1.0 + fixed_size: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + + loggers: + wandb: + enable: false + project: 'threestudio' + + loss: + lambda_sds: 0. + lambda_orient: [0, 10.0, 1000., 5000.0] + lambda_sparsity: 1.0 + lambda_opaque: 1.0 + lambda_l1: 10. + lambda_p: 10. + lambda_kl: 0.000001 + lambda_G: 0.01 + lambda_D: 1. + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry: + lr: 0.01 + background: + lr: 0.001 + renderer.generator: + lr: 0.0001 + renderer.local_encoder: + lr: 0.0001 + renderer.global_encoder: + lr: 0.0001 + optimizer_dis: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + params: + renderer.discriminator: + lr: 0.00001 + +trainer: + max_steps: 50000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/debugging/controlnet-canny.yaml b/configs/debugging/controlnet-canny.yaml new file mode 100644 index 0000000..1f3beff --- /dev/null +++ b/configs/debugging/controlnet-canny.yaml @@ -0,0 +1,13 @@ +system: + guidance_type: "controlnet-guidance" + guidance: + control_type: "canny" + min_step_percent: 0.8 + max_step_percent: 0.98 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + prompt: "Elon Musk, RAW photo, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3" + # negative_prompt: "(overexposed, underexposed, out of focus, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck" + negative_prompt: "(overexposed, underexposed, out of focus, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4)" diff --git a/configs/debugging/controlnet-normal.yaml b/configs/debugging/controlnet-normal.yaml new file mode 100644 index 0000000..4a0ba4e --- /dev/null +++ b/configs/debugging/controlnet-normal.yaml @@ -0,0 +1,12 @@ +system: + guidance_type: "stable-diffusion-controlnet-guidance" + guidance: + control_type: "normal" + min_step_percent: 0.05 + max_step_percent: 0.8 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "SG161222/Realistic_Vision_V2.0" + prompt: "Elon Musk, RAW photo, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3" + negative_prompt: "(overexposed, underexposed, out of focus, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4)" diff --git a/configs/debugging/instructpix2pix.yaml b/configs/debugging/instructpix2pix.yaml new file mode 100644 index 0000000..0ff9cdc --- /dev/null +++ b/configs/debugging/instructpix2pix.yaml @@ -0,0 +1,10 @@ +system: + guidance_type: "stable-diffusion-instructpix2pix-guidance" + guidance: + min_step_percent: 0.8 + max_step_percent: 0.98 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + prompt: "Turn him into Elon Musk" diff --git a/configs/debugging/stablediffusion.yaml b/configs/debugging/stablediffusion.yaml new file mode 100644 index 0000000..4ab38a8 --- /dev/null +++ b/configs/debugging/stablediffusion.yaml @@ -0,0 +1,15 @@ +system: + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: "A cute panda" + front_threshold: 30. + back_threshold: 30. + + guidance_type: "stable-diffusion-vsd-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + pretrained_model_name_or_path_lora: "stabilityai/stable-diffusion-2-1" + guidance_scale: 7.5 + min_step_percent: 0.02 + max_step_percent: 0.98 diff --git a/configs/dreamfusion-if.yaml b/configs/dreamfusion-if.yaml new file mode 100644 index 0000000..393f71f --- /dev/null +++ b/configs/dreamfusion-if.yaml @@ -0,0 +1,112 @@ +name: "dreamfusion-if" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: 1 + width: 64 + height: 64 + camera_distance_range: [1.5, 2.0] + fovy_range: [40, 70] + elevation_range: [-10, 90] + light_sample_strategy: "dreamfusion" + eval_camera_distance: 2.0 + eval_fovy_deg: 70. + +system_type: "dreamfusion-system" +system: + geometry_type: "implicit-volume" + geometry: + radius: 2.0 + normal_type: "analytic" + + # the density initialization proposed in the DreamFusion paper + # does not work very well + # density_bias: "blob_dreamfusion" + # density_activation: exp + # density_blob_scale: 5. + # density_blob_std: 0.2 + + # use Magic3D density initialization instead + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + # coarse to fine hash grid encoding + # to ensure smooth analytic normals + pos_encoding_config: + otype: ProgressiveBandHashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + start_level: 8 # resolution ~200 + start_step: 2000 + update_steps: 500 + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 2001 + albedo_activation: scale_-11_01 + + background_type: "neural-environment-map-background" + background: + color_activation: scale_-11_01 + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + + prompt_processor_type: "deep-floyd-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + prompt: ??? + + guidance_type: "deep-floyd-guidance" + guidance: + pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + guidance_scale: 20. + weighting_strategy: sds + min_step_percent: 0.02 + max_step_percent: 0.98 + + loggers: + wandb: + enable: false + project: 'threestudio' + name: None + + loss: + lambda_sds: 1. + lambda_orient: [0, 10., 1000., 5000] + lambda_sparsity: 1. + lambda_opaque: 0.0 + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry: + lr: 0.01 + background: + lr: 0.001 + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/dreamfusion-sd-eff.yaml b/configs/dreamfusion-sd-eff.yaml new file mode 100644 index 0000000..88a23aa --- /dev/null +++ b/configs/dreamfusion-sd-eff.yaml @@ -0,0 +1,115 @@ +name: "dreamfusion-sd" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "eff-random-camera-datamodule" +data: + batch_size: 1 + width: 128 + height: 128 + sample_width: 64 + sample_height: 64 + camera_distance_range: [1.5, 2.0] + fovy_range: [40, 70] + elevation_range: [-10, 45] + light_sample_strategy: "dreamfusion" + eval_camera_distance: 2.0 + eval_fovy_deg: 70. + +system_type: "efficient-dreamfusion-system" +system: + geometry_type: "implicit-volume" + geometry: + radius: 2.0 + normal_type: "analytic" + + # the density initialization proposed in the DreamFusion paper + # does not work very well + # density_bias: "blob_dreamfusion" + # density_activation: exp + # density_blob_scale: 5. + # density_blob_std: 0.2 + + # use Magic3D density initialization instead + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + # coarse to fine hash grid encoding + # to ensure smooth analytic normals + pos_encoding_config: + otype: ProgressiveBandHashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + start_level: 8 # resolution ~200 + start_step: 2000 + update_steps: 500 + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 2001 + albedo_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 100. + weighting_strategy: sds + min_step_percent: 0.02 + max_step_percent: 0.98 + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 1. + lambda_orient: [0, 10., 1000., 5000] + lambda_sparsity: 1. + lambda_opaque: 0. + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry: + lr: 0.01 + background: + lr: 0.001 + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/dreamfusion-sd.yaml b/configs/dreamfusion-sd.yaml new file mode 100644 index 0000000..c6a0f8f --- /dev/null +++ b/configs/dreamfusion-sd.yaml @@ -0,0 +1,113 @@ +name: "dreamfusion-sd" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: 1 + width: 64 + height: 64 + camera_distance_range: [1.5, 2.0] + fovy_range: [40, 70] + elevation_range: [-10, 45] + light_sample_strategy: "dreamfusion" + eval_camera_distance: 2.0 + eval_fovy_deg: 70. + +system_type: "dreamfusion-system" +system: + geometry_type: "implicit-volume" + geometry: + radius: 2.0 + normal_type: "analytic" + + # the density initialization proposed in the DreamFusion paper + # does not work very well + # density_bias: "blob_dreamfusion" + # density_activation: exp + # density_blob_scale: 5. + # density_blob_std: 0.2 + + # use Magic3D density initialization instead + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + # coarse to fine hash grid encoding + # to ensure smooth analytic normals + pos_encoding_config: + otype: ProgressiveBandHashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + start_level: 8 # resolution ~200 + start_step: 2000 + update_steps: 500 + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 2001 + albedo_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 100. + weighting_strategy: sds + min_step_percent: 0.02 + max_step_percent: 0.98 + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 1. + lambda_orient: [0, 10., 1000., 5000] + lambda_sparsity: 1. + lambda_opaque: 0. + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry: + lr: 0.01 + background: + lr: 0.001 + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/experimental/co3d-imagecondition.yaml b/configs/experimental/co3d-imagecondition.yaml new file mode 100644 index 0000000..c095073 --- /dev/null +++ b/configs/experimental/co3d-imagecondition.yaml @@ -0,0 +1,133 @@ +name: "co3d-imagecondition" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "co3d-datamodule" +data: + root_dir: ??? + height: 256 + width: 256 + scale_radius: 3.0 + load_preprocessed: false + cam_scale_factor: 0.95 # inherited from plenoxels + max_num_frames: 300 # use less frames for debugging + v2_mode: true + use_mask: true + box_crop: true + box_crop_mask_thr: 0.4 + box_crop_context: 0.1 # The amount of additional padding added to each dimention of the cropping bounding box, relative to vox size. + train_num_rays: 4096 + train_split: "train" + val_split: "val" + test_split: "test" + render_path: "circle" + train_views: [0, 50, 100] + random_camera: + eval_height: 256 + eval_width: 256 + eval_elevation_deg: 0. + eval_camera_distance: 1.2 + eval_fovy_deg: 60. + +system_type: "image-condition-dreamfusion-system" +system: + geometry_type: "implicit-volume" + geometry: + isosurface_method: "mc-cpu" + isosurface_resolution: 128 + isosurface_threshold: 0.0 + normal_type: "finite_difference" + finite_difference_normal_eps: 0.004 + n_feature_dims: 32 + mlp_network_config: + otype: "VanillaMLP" + activation: "ReLU" + output_activation: "none" + n_neurons: 64 + n_hidden_layers: 2 + + material_type: "diffuse-with-point-light-material" + material: + diffuse_prob: 1.0 + textureless_prob: 0.2 + ambient_light_color: [1.0, 1.0, 1.0] + diffuse_light_color: [0.0, 0.0, 0.0] + ambient_only_steps: ${system.freq.ref_only_steps} + + background_type: "neural-environment-map-background" + background: + dir_encoding_config: + otype: ProgressiveBandFrequency + n_frequencies: 6 + mlp_network_config: + otype: VanillaMLP + n_neurons: 32 + n_hidden_layers: 1 + activation: "ReLU" + + renderer_type: "nerf-volume-renderer" + renderer: + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + guidance_scale: 100. + weighting_strategy: sds + + freq: + n_ref: 2 + ref_only_steps: 1000 + + loggers: + wandb: + enable: false + project: 'threestudio' + name: None + + loss: + lambda_sds: 0.1 + lambda_rgb: 10. + lambda_mask: 1. + lambda_depth: 0. + # lambda_depth: [0.0, 0.0, 1.0, 10000] + lambda_normal_smooth: 0.0 + lambda_orient: 1.0 + # lambda_orient: [1000, 0.0, 10, 6000] + lambda_sparsity: 0.0 + lambda_opaque: 0.01 + + optimizer: + name: Adan + args: + eps: 1.0e-8 + weight_decay: 2.0e-5 + max_grad_norm: 5.0 + foreach: False + params: + geometry.encoding: + lr: 0.05 + geometry.network: + lr: 0.005 + background.network: + lr: 0.005 + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 500 + limit_val_batches: 6 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/experimental/imagecondition.yaml b/configs/experimental/imagecondition.yaml new file mode 100644 index 0000000..8e923c8 --- /dev/null +++ b/configs/experimental/imagecondition.yaml @@ -0,0 +1,136 @@ +name: "imagecondition" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "single-image-datamodule" +data: + image_path: ./load/images/hamburger_rgba.png + requires_depth: ${cmaxgt0orcmaxgt0:${system.loss.lambda_depth},${system.loss.lambda_depth_rel}} + requires_normal: ${cmaxgt0:${system.loss.lambda_normal}} + random_camera: + batch_size: 4 + eval_height: 256 + eval_width: 256 + eval_elevation_deg: 0. + eval_camera_distance: 1.2 + eval_fovy_deg: 60. + n_val_views: 30 + n_test_views: 120 + +system_type: "image-condition-dreamfusion-system" +system: + geometry_type: "implicit-volume" + geometry: + isosurface_method: "mc-cpu" + isosurface_resolution: 128 + isosurface_threshold: 5.0 + normal_type: "finite_difference" + finite_difference_normal_eps: 0.004 + mlp_network_config: + otype: "VanillaMLP" + activation: "ReLU" + output_activation: "none" + n_neurons: 64 + n_hidden_layers: 2 + + material_type: "diffuse-with-point-light-material" + material: + diffuse_prob: 1.0 + textureless_prob: 0.2 + ambient_light_color: [1.0, 1.0, 1.0] + diffuse_light_color: [0.0, 0.0, 0.0] + ambient_only_steps: ${system.freq.ref_only_steps} + + background_type: "neural-environment-map-background" + background: + dir_encoding_config: + otype: ProgressiveBandFrequency + n_frequencies: 6 + mlp_network_config: + otype: VanillaMLP + n_neurons: 32 + n_hidden_layers: 1 + activation: "ReLU" + + renderer_type: "nerf-volume-renderer" + renderer: + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + prompt: "a DSLR photo of a delicious hamburger" + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + guidance_scale: 100. + weighting_strategy: sds # sds, uniform, fantasia3d + + # prompt_processor_type: "deep-floyd-prompt-processor" + # prompt_processor: + # pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + # prompt: "a DSLR photo of a delicious hamburger" + + # guidance_type: "deep-floyd-guidance" + # guidance: + # pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + # guidance_scale: 20. + # weighting_strategy: sds # sds, uniform, fantasia3d + + freq: + n_ref: 2 + ref_only_steps: 100 + guidance_eval: 0 + + loggers: + wandb: + enable: false + project: 'threestudio' + name: None + + loss: + lambda_sds: 0.1 + lambda_rgb: 10. + lambda_mask: 1. + lambda_depth: [0.0, 0.0, 1.0, 10000] + lambda_depth_rel: [0.0, 0.0, 1.0, 10000] + lambda_normal: 0. # [0, 0, 0.05, 100] + lambda_normal_smooth: 0.0 + lambda_3d_normal_smooth: 0.0 + lambda_orient: 0.0 + lambda_sparsity: 0.0 + lambda_opaque: 0.0 + + + optimizer: + name: Adan + args: + eps: 1.0e-8 + weight_decay: 2.0e-5 + max_grad_norm: 5.0 + foreach: False + params: + geometry.encoding: + lr: 0.05 + geometry.density_network: + lr: 0.005 + geometry.feature_network: + lr: 0.005 + background.network: + lr: 0.005 + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 100 + limit_val_batches: 6 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/experimental/imagecondition_zero123nerf.yaml b/configs/experimental/imagecondition_zero123nerf.yaml new file mode 100644 index 0000000..c172c47 --- /dev/null +++ b/configs/experimental/imagecondition_zero123nerf.yaml @@ -0,0 +1,166 @@ +name: "imagecondition" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "single-image-datamodule" +data: + image_path: ./load/images/hamburger_rgba.png + height: 256 + width: 256 + default_elevation_deg: 0.0 + default_azimuth_deg: 0.0 + default_camera_distance: 3.8 + default_fovy_deg: 20.0 + requires_depth: ${cmaxgt0orcmaxgt0:${system.loss.lambda_depth},${system.loss.lambda_depth_rel}} + requires_normal: ${cmaxgt0:${system.loss.lambda_normal}} + random_camera: + height: 128 + width: 128 + batch_size: 12 + resolution_milestones: [] + eval_height: 256 + eval_width: 256 + eval_batch_size: 1 + elevation_range: [-10, 80] + azimuth_range: [-180, 180] + camera_distance_range: [3.8, 3.8] + fovy_range: [20.0, 20.0] # Zero123 has fixed fovy + progressive_until: 0 + camera_perturb: 0.0 + center_perturb: 0.0 + up_perturb: 0.0 + light_position_perturb: 1.0 + light_distance_range: [7.5, 10.0] + eval_elevation_deg: ${data.default_elevation_deg} + eval_camera_distance: ${data.default_camera_distance} + eval_fovy_deg: ${data.default_fovy_deg} + light_sample_strategy: "dreamfusion" + batch_uniform_azimuth: False + n_val_views: 30 + n_test_views: 120 + +system_type: "image-condition-dreamfusion-system" +system: + geometry_type: "implicit-volume" + geometry: + radius: 2.0 + normal_type: "analytic" + + # the density initialization proposed in the DreamFusion paper + # does not work very well + # density_bias: "blob_dreamfusion" + # density_activation: exp + # density_blob_scale: 5. + # density_blob_std: 0.2 + + # use Magic3D density initialization instead + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + # coarse to fine hash grid encoding + # to ensure smooth analytic normals + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + mlp_network_config: + otype: "VanillaMLP" + activation: "ReLU" + output_activation: "none" + n_neurons: 64 + n_hidden_layers: 2 + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 100000 + textureless_prob: 0.05 + albedo_activation: sigmoid + + # background_type: "neural-environment-map-background" + # background: + # color_activation: sigmoid + + background_type: "solid-color-background" + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + return_comp_normal: ${gt0:${system.loss.lambda_normal_smooth}} + return_normal_perturb: ${gt0:${system.loss.lambda_3d_normal_smooth}} + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + guidance_scale: 100 + min_step_percent: [0, 0.4, 0.2, 200] # (start_iter, start_val, end_val, end_iter) + max_step_percent: [0, 0.85, 0.5, 200] + + # prompt_processor_type: "deep-floyd-prompt-processor" + # prompt_processor: + # pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + # prompt: "a DSLR photo of a delicious hamburger" + + # guidance_type: "deep-floyd-guidance" + # guidance: + # pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + # guidance_scale: 7.5 + # min_step_percent: 0.2 + # # min_step_percent: [0, 0.66, 0.33, 2000] # (start_iter, start_val, end_val, end_iter) + # max_step_percent: 0.6 + # # max_step_percent: [0, 0.98, 0.66, 2000] + + freq: + ref_only_steps: 0 + guidance_eval: 0 + + loggers: + wandb: + enable: false + project: 'threestudio' + name: None + + loss: + lambda_sds: 10.0 + lambda_rgb: 0. + lambda_mask: 0. + lambda_depth: 0. + lambda_depth_rel: 0. # [0.0, 0.0, 1.0, 10000] + lambda_normal: 0. # [0, 0, 0.05, 100] + lambda_normal_smooth: 10.0 + lambda_3d_normal_smooth: 10.0 + lambda_orient: 1.0 + lambda_sparsity: 0.1 + lambda_opaque: 0.1 + + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-8 + +trainer: + max_steps: 200 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 20 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: 20 # ${trainer.max_steps} diff --git a/configs/experimental/imagecondition_zero123nerf_refine.yaml b/configs/experimental/imagecondition_zero123nerf_refine.yaml new file mode 100644 index 0000000..bbeb4d6 --- /dev/null +++ b/configs/experimental/imagecondition_zero123nerf_refine.yaml @@ -0,0 +1,160 @@ +name: "imagecondition" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +# data_type: "single-image-datamodule" +# data: +# image_path: ./load/images/hamburger_rgba.png +# height: 512 +# width: 512 +# default_elevation_deg: 0.0 +# default_azimuth_deg: 0.0 +# default_camera_distance: 3.8 +# default_fovy_deg: 20.0 +# requires_depth: ${cmaxgt0orcmaxgt0:${system.loss.lambda_depth},${system.loss.lambda_depth_rel}} +# requires_normal: ${cmaxgt0:${system.loss.lambda_normal}} +# random_camera: + +data_type: "random-camera-datamodule" +data: + height: 512 + width: 512 + batch_size: 2 + resolution_milestones: [] + eval_height: 512 + eval_width: 512 + eval_batch_size: 1 + elevation_range: [-10, 45] + azimuth_range: [-180, 180] + camera_distance_range: [3.2, 3.8] + fovy_range: [10.0, 15.0] # Zero123 has fixed fovy + progressive_until: 0 + camera_perturb: 0.0 + center_perturb: 0.0 + up_perturb: 0.0 + light_position_perturb: 1.0 + light_distance_range: [7.5, 10.0] + eval_elevation_deg: ${data.default_elevation_deg} + eval_camera_distance: ${data.default_camera_distance} + eval_fovy_deg: ${data.default_fovy_deg} + light_sample_strategy: "dreamfusion" + batch_uniform_azimuth: False + n_val_views: 30 + n_test_views: 120 + +system_type: "image-condition-dreamfusion-system" +system: + refinement: true + geometry_convert_from: ??? + geometry_convert_inherit_texture: true + geometry_type: "tetrahedra-sdf-grid" + geometry: + radius: 2.0 # consistent with coarse + isosurface_resolution: 128 + isosurface_deformable_grid: true + pos_encoding_config: # consistent with coarse, no progressive band + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.4472692374403782 # max resolution 4096 + mlp_network_config: + otype: "VanillaMLP" + activation: "ReLU" + output_activation: "none" + n_neurons: 64 + n_hidden_layers: 2 + fix_geometry: false # optimize grid sdf and deformation + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 0 + soft_shading: true + + # background_type: "neural-environment-map-background" + background_type: "solid-color-background" + + # renderer_type: "nerf-volume-renderer" + # renderer: + # radius: ${system.geometry.radius} + # num_samples_per_ray: 512 + # return_comp_normal: ${gt0:${system.loss.lambda_normal_smooth}} + # return_normal_perturb: ${gt0:${system.loss.lambda_3d_normal_smooth}} + + renderer_type: "nvdiff-rasterizer" + renderer: + context_type: cuda + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 50 + min_step_percent: [0, 0.4, 0.1, 200] # (start_iter, start_val, end_val, end_iter) + max_step_percent: [0, 0.7, 0.4, 200] + + # prompt_processor_type: "deep-floyd-prompt-processor" + # prompt_processor: + # pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + # prompt: "a DSLR photo of a delicious hamburger" + + # guidance_type: "deep-floyd-guidance" + # guidance: + # pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + # guidance_scale: 7.5 + # min_step_percent: 0.2 + # # min_step_percent: [0, 0.66, 0.33, 2000] # (start_iter, start_val, end_val, end_iter) + # max_step_percent: 0.6 + # # max_step_percent: [0, 0.98, 0.66, 2000] + + freq: + ref_only_steps: 0 + guidance_eval: 0 + + loggers: + wandb: + enable: false + project: 'threestudio' + name: None + + loss: + lambda_sds: 5.0 + lambda_rgb: 0.0 + lambda_mask: 0.0 + lambda_depth: 0. + lambda_depth_rel: 0. # [0.0, 0.0, 1.0, 10000] + lambda_normal: 0. # [0, 0, 0.05, 100] + lambda_normal_smooth: 0. + lambda_3d_normal_smooth: 0. + lambda_normal_consistency: 100000. + lambda_laplacian_smoothness: 0. + lambda_orient: 0. + lambda_sparsity: 0. + lambda_opaque: 0. + + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-8 + +trainer: + max_steps: 200 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 5 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: 20 # ${trainer.max_steps} diff --git a/configs/experimental/prolificdreamer-importance.yaml b/configs/experimental/prolificdreamer-importance.yaml new file mode 100644 index 0000000..a1209d2 --- /dev/null +++ b/configs/experimental/prolificdreamer-importance.yaml @@ -0,0 +1,115 @@ +name: "prolificdreamer-importance" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: 1 + width: 512 + height: 512 + camera_distance_range: [1.0, 1.5] + fovy_range: [40, 70] + elevation_range: [-10, 45] + camera_perturb: 0. + center_perturb: 0. + up_perturb: 0. + eval_camera_distance: 1.5 + eval_fovy_deg: 70. + +system_type: "prolificdreamer-system" +system: + stage: coarse + geometry_type: "implicit-volume" + geometry: + radius: 1.0 + normal_type: null + + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + + material_type: "no-material" + material: + n_output_dims: 3 + color_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + random_aug: true + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + estimator: importance + num_samples_per_ray: 64 + num_samples_per_ray_importance: 32 + near_plane: 0.1 + far_plane: 4.0 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + front_threshold: 30. + back_threshold: 30. + + guidance_type: "stable-diffusion-vsd-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + pretrained_model_name_or_path_lora: "stabilityai/stable-diffusion-2-1" + guidance_scale: 7.5 + min_step_percent: 0.02 + max_step_percent: [5000, 0.98, 0.5, 5001] # annealed to 0.5 after 5000 steps + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_vsd: 1. + lambda_lora: 1. + lambda_orient: 0. + lambda_sparsity: 10. + lambda_opaque: [10000, 0.0, 1000.0, 10001] + optimizer: + name: AdamW + args: + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry.encoding: + lr: 0.01 + geometry.density_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + background: + lr: 0.001 + guidance: + lr: 0.0001 + +trainer: + max_steps: 25000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/experimental/prolificdreamer-neus-importance.yaml b/configs/experimental/prolificdreamer-neus-importance.yaml new file mode 100644 index 0000000..08ba0b6 --- /dev/null +++ b/configs/experimental/prolificdreamer-neus-importance.yaml @@ -0,0 +1,127 @@ +name: "prolificdreamer-neus-importance" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: [1, 1] + # 0-4999: 64x64, >=5000: 512x512 + # this drastically reduces VRAM usage as empty space is pruned in early training + width: [64, 128] + height: [64, 128] + resolution_milestones: [5000] + camera_distance_range: [1.0, 1.5] + fovy_range: [40, 70] + elevation_range: [-10, 45] + camera_perturb: 0. + center_perturb: 0. + up_perturb: 0. + eval_camera_distance: 1.5 + eval_fovy_deg: 70. + +system_type: "prolificdreamer-system" +system: + stage: coarse + geometry_type: "implicit-sdf" + geometry: + radius: 2.0 + normal_type: finite_difference + # progressive eps from Neuralangelo + finite_difference_normal_eps: progressive + + sdf_bias: sphere + sdf_bias_params: 0.5 + + # coarse to fine hash grid encoding + pos_encoding_config: + otype: ProgressiveBandHashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.381912879967776 # max resolution 2048 + start_level: 8 # resolution ~200 + start_step: 2000 + update_steps: 500 + + material_type: no-material + material: + n_output_dims: 3 + color_activation: sigmoid + + background_type: "solid-color-background" + background: + n_output_dims: 3 + color: [0, 0, 0] + + renderer_type: neus-volume-renderer + renderer: + radius: ${system.geometry.radius} + use_volsdf: true + + estimator: importance + num_samples_per_ray: 64 + num_samples_per_ray_importance: 128 + near_plane: 0.1 + far_plane: 4.0 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + front_threshold: 30. + back_threshold: 30. + + guidance_type: "stable-diffusion-vsd-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + pretrained_model_name_or_path_lora: "stabilityai/stable-diffusion-2-1" + guidance_scale: 7.5 + min_step_percent: 0.02 + max_step_percent: [5000, 0.98, 0.5, 5001] # annealed to 0.5 after 5000 steps + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_vsd: 1. + lambda_lora: 1. + lambda_orient: 0. + lambda_sparsity: 0. + lambda_opaque: 0 + lambda_eikonal: 100. + optimizer: + name: AdamW + args: + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry.encoding: + lr: 0.01 + geometry.sdf_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + background: + lr: 0.001 + renderer: + lr: 0.001 + guidance: + lr: 0.0001 + +trainer: + max_steps: 25000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/experimental/prolificdreamer-propnet.yaml b/configs/experimental/prolificdreamer-propnet.yaml new file mode 100644 index 0000000..2f795b6 --- /dev/null +++ b/configs/experimental/prolificdreamer-propnet.yaml @@ -0,0 +1,156 @@ +name: "prolificdreamer-propnet" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: [1, 1] + # 0-4999: 64x64, >=5000: 512x512 + # this drastically reduces VRAM usage as empty space is pruned in early training + width: [64, 512] + height: [64, 512] + resolution_milestones: [5000] + camera_distance_range: [1.0, 1.5] + fovy_range: [40, 70] + elevation_range: [-10, 45] + camera_perturb: 0. + center_perturb: 0. + up_perturb: 0. + eval_camera_distance: 1.5 + eval_fovy_deg: 70. + +system_type: "prolificdreamer-system" +system: + stage: coarse + geometry_type: "implicit-volume" + geometry: + radius: 1.0 + normal_type: null + + density_bias: blob_magic3d + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + + material_type: no-material + material: + n_output_dims: 3 + color_activation: sigmoid + + background_type: neural-environment-map-background + background: + color_activation: sigmoid + random_aug: true + + renderer_type: nerf-volume-renderer + renderer: + radius: ${system.geometry.radius} + estimator: proposal + num_samples_per_ray: 32 + num_samples_per_ray_proposal: 64 + near_plane: 0.1 + far_plane: 4.0 + proposal_network_config: + n_input_dims: 3 + n_output_dims: 1 + encoding_config: + otype: HashGrid + n_levels: 5 + n_features_per_level: 2 + log2_hashmap_size: 17 + base_resolution: 16 + per_level_scale: 1.681792830507429 # max_resolution: 128 + network_config: + otype: VanillaMLP + activation: ReLU + output_activation: none + n_neurons: 64 + n_hidden_layers: 1 + prop_optimizer_config: + name: Adam + args: + lr: 1.0e-2 + eps: 1.0e-15 + weight_decay: 1.0e-6 + prop_scheduler_config: + name: ChainedScheduler + schedulers: + - name: LinearLR + args: + start_factor: 0.01 + total_iters: 100 + # - + # name: MultiStepLR + # args: + # milestones: + # - ${idiv:${trainer.max_steps},2} + # - ${idiv:${mul:${trainer.max_steps},3},4} + # - ${idiv:${mul:${trainer.max_steps},9},10} + # gamma: 0.33 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + front_threshold: 30. + back_threshold: 30. + + guidance_type: "stable-diffusion-vsd-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + pretrained_model_name_or_path_lora: "stabilityai/stable-diffusion-2-1" + guidance_scale: 7.5 + min_step_percent: 0.02 + max_step_percent: [5000, 0.98, 0.5, 5001] # annealed to 0.5 after 5000 steps + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_vsd: 1. + lambda_lora: 1. + lambda_orient: 0. + lambda_sparsity: 10. + lambda_opaque: [10000, 0.0, 1000.0, 10001] + lambda_z_variance: 0. + optimizer: + name: AdamW + args: + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry.encoding: + lr: 0.01 + geometry.density_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + background: + lr: 0.001 + guidance: + lr: 0.0001 + +trainer: + max_steps: 25000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/experimental/textmesh-if-importance.yaml b/configs/experimental/textmesh-if-importance.yaml new file mode 100644 index 0000000..75f7227 --- /dev/null +++ b/configs/experimental/textmesh-if-importance.yaml @@ -0,0 +1,111 @@ +name: "textmesh-if-importance" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: 1 + width: 64 + height: 64 + camera_distance_range: [1.5, 2.0] + fovy_range: [40, 70] + elevation_range: [-10, 90] + light_sample_strategy: "dreamfusion" + eval_camera_distance: 2.0 + eval_fovy_deg: 70. + +system_type: "textmesh-system" +system: + geometry_type: "implicit-sdf" + geometry: + radius: 2.0 + normal_type: finite_difference + # progressive eps from Neuralangelo + finite_difference_normal_eps: progressive + + sdf_bias: sphere + sdf_bias_params: 0.5 + + # coarse to fine hash grid encoding + pos_encoding_config: + otype: ProgressiveBandHashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.381912879967776 # max resolution 2048 + start_level: 8 # resolution ~200 + start_step: 2000 + update_steps: 500 + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 200100 + albedo_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + + renderer_type: "neus-volume-renderer" + renderer: + radius: ${system.geometry.radius} + cos_anneal_end_steps: ${trainer.max_steps} + use_volsdf: true + eval_chunk_size: 8192 + + estimator: importance + num_samples_per_ray: 64 + num_samples_per_ray_importance: 128 + near_plane: 0.1 + far_plane: 4.0 + + prompt_processor_type: "deep-floyd-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + prompt: ??? + + guidance_type: "deep-floyd-guidance" + guidance: + pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + guidance_scale: 20. + weighting_strategy: sds + min_step_percent: 0.02 + max_step_percent: 0.98 + + loss: + lambda_sds: 1. + lambda_orient: 0.0 + lambda_sparsity: 0.0 + lambda_opaque: 0.0 + lambda_eikonal: 10. + optimizer: + name: Adam + args: + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry.encoding: + lr: 0.01 + geometry.sdf_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + background: + lr: 0.001 + renderer: + lr: 0.0001 + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/experimental/unified-guidance/dreamfusion-sd.yaml b/configs/experimental/unified-guidance/dreamfusion-sd.yaml new file mode 100644 index 0000000..2468eca --- /dev/null +++ b/configs/experimental/unified-guidance/dreamfusion-sd.yaml @@ -0,0 +1,115 @@ +name: "dreamfusion-sd" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: 1 + width: 64 + height: 64 + camera_distance_range: [1.5, 2.0] + fovy_range: [40, 70] + elevation_range: [-10, 45] + light_sample_strategy: "dreamfusion" + eval_camera_distance: 2.0 + eval_fovy_deg: 70. + +system_type: "dreamfusion-system" +system: + geometry_type: "implicit-volume" + geometry: + radius: 2.0 + normal_type: "analytic" + + # the density initialization proposed in the DreamFusion paper + # does not work very well + # density_bias: "blob_dreamfusion" + # density_activation: exp + # density_blob_scale: 5. + # density_blob_std: 0.2 + + # use Magic3D density initialization instead + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + # coarse to fine hash grid encoding + # to ensure smooth analytic normals + pos_encoding_config: + otype: ProgressiveBandHashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + start_level: 8 # resolution ~200 + start_step: 2000 + update_steps: 500 + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 2001 + albedo_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-unified-guidance" + guidance: + guidance_type: "sds" + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 100. + weighting_strategy: dreamfusion + min_step_percent: 0.02 + max_step_percent: 0.98 + use_img_loss: false + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sd: 1. + lambda_orient: [0, 10., 1000., 5000] + lambda_sparsity: 1. + lambda_opaque: 0. + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry: + lr: 0.01 + background: + lr: 0.001 + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/experimental/unified-guidance/hifa.yaml b/configs/experimental/unified-guidance/hifa.yaml new file mode 100644 index 0000000..9a42ee9 --- /dev/null +++ b/configs/experimental/unified-guidance/hifa.yaml @@ -0,0 +1,112 @@ +name: "hifa-unified" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: 1 + width: 512 + height: 512 + camera_distance_range: [1.0, 1.5] + fovy_range: [40, 70] + elevation_range: [-10, 45] + camera_perturb: 0. + center_perturb: 0. + up_perturb: 0. + eval_camera_distance: 1.5 + eval_fovy_deg: 70. + +system_type: "dreamfusion-system" +system: + geometry_type: "implicit-volume" + geometry: + radius: 1.0 + normal_type: null + + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + + material_type: "no-material" + material: + n_output_dims: 3 + color_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + random_aug: true + + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-unified-guidance" + guidance: + guidance_type: "sds" + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 100. + weighting_strategy: dreamfusion + min_step_percent: 0.3 + max_step_percent: 0.98 + use_img_loss: true + sqrt_anneal: true + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sd: 1. + lambda_sd_img: 0.01 + lambda_orient: 0. + lambda_sparsity: 1. + lambda_opaque: 0. + lambda_z_variance: 100. + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry.encoding: + lr: 0.01 + geometry.density_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + background: + lr: 0.001 +trainer: + max_steps: 25000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/experimental/unified-guidance/prolificdreamer-hifa.yaml b/configs/experimental/unified-guidance/prolificdreamer-hifa.yaml new file mode 100644 index 0000000..512ae7e --- /dev/null +++ b/configs/experimental/unified-guidance/prolificdreamer-hifa.yaml @@ -0,0 +1,120 @@ +name: "prolificdreamer-hifa-unified" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: [1, 1] + # 0-4999: 64x64, >=5000: 512x512 + # this drastically reduces VRAM usage as empty space is pruned in early training + width: [64, 512] + height: [64, 512] + resolution_milestones: [5000] + camera_distance_range: [1.0, 1.5] + fovy_range: [40, 70] + elevation_range: [-10, 45] + camera_perturb: 0. + center_perturb: 0. + up_perturb: 0. + eval_camera_distance: 1.5 + eval_fovy_deg: 70. + +system_type: "prolificdreamer-system" +system: + stage: coarse + geometry_type: "implicit-volume" + geometry: + radius: 1.0 + normal_type: null + + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + + material_type: "no-material" + material: + n_output_dims: 3 + color_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + random_aug: true + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + front_threshold: 30. + back_threshold: 30. + + guidance_type: "stable-diffusion-unified-guidance" + guidance: + guidance_type: "vsd" + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 7.5 + weighting_strategy: dreamfusion + min_step_percent: 0.3 + max_step_percent: 0.98 + vsd_phi_model_name_or_path: "stabilityai/stable-diffusion-2-1" + sqrt_anneal: true + use_img_loss: true + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sd: 1. + lambda_sd_img: 0.01 + lambda_train_phi: 1. + lambda_orient: 0. + lambda_sparsity: 10. + lambda_opaque: [10000, 0.0, 1000.0, 10001] + lambda_z_variance: 300. + optimizer: + name: AdamW + args: + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry.encoding: + lr: 0.01 + geometry.density_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + background: + lr: 0.001 + guidance: + lr: 0.0001 + +trainer: + max_steps: 25000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/experimental/unified-guidance/prolificdreamer.yaml b/configs/experimental/unified-guidance/prolificdreamer.yaml new file mode 100644 index 0000000..aeedc2d --- /dev/null +++ b/configs/experimental/unified-guidance/prolificdreamer.yaml @@ -0,0 +1,120 @@ +name: "prolificdreamer" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: [1, 1] + # 0-4999: 64x64, >=5000: 512x512 + # this drastically reduces VRAM usage as empty space is pruned in early training + width: [256, 256] + height: [256, 256] + resolution_milestones: [2000] + camera_distance_range: [1.0, 1.5] + fovy_range: [40, 70] + elevation_range: [-10, 45] + camera_perturb: 0. + center_perturb: 0. + up_perturb: 0. + eval_camera_distance: 1.5 + eval_fovy_deg: 70. + +system_type: "prolificdreamer-system" +system: + stage: coarse + geometry_type: "implicit-volume" + geometry: + radius: 1.0 + normal_type: null + + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + + material_type: "no-material" + material: + n_output_dims: 3 + color_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + random_aug: true + random_aug_prob: 0.1 + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + front_threshold: 30. + back_threshold: 30. + + guidance_type: "stable-diffusion-unified-guidance" + guidance: + guidance_type: "vsd" + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 7.5 + weighting_strategy: dreamfusion + min_step_percent: 0.02 + max_step_percent: [5000, 0.98, 0.5, 5001] # annealed to 0.5 after 5000 steps + vsd_phi_model_name_or_path: "stabilityai/stable-diffusion-2-1" + use_img_loss: false + sqrt_anneal: false + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sd: 1. + lambda_train_phi: 1. + lambda_orient: 0. + lambda_sparsity: 10. + lambda_opaque: [10000, 0.0, 1000.0, 10001] + lambda_z_variance: 0. + optimizer: + name: AdamW + args: + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry.encoding: + lr: 0.01 + geometry.density_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + background: + lr: 0.001 + guidance: + lr: 0.0001 + +trainer: + max_steps: 25000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/experimental/unified-guidance/zero123-simple.yaml b/configs/experimental/unified-guidance/zero123-simple.yaml new file mode 100644 index 0000000..3375b80 --- /dev/null +++ b/configs/experimental/unified-guidance/zero123-simple.yaml @@ -0,0 +1,141 @@ +name: "zero123-simple" +tag: "${system.guidance.guidance_type}_${data.random_camera.height}_${rmspace:${basename:${data.image_path}},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "single-image-datamodule" +data: + image_path: ??? + height: 256 + width: 256 + default_elevation_deg: 0.0 + default_azimuth_deg: 0.0 + default_camera_distance: 2.5 + default_fovy_deg: 40.0 + random_camera: + batch_size: 1 + height: 256 + width: 256 + elevation_range: [-45, 45] + azimuth_range: [-180, 180] + camera_distance_range: [2.5, 2.5] + fovy_range: [40.0, 40.0] + camera_perturb: 0.0 + center_perturb: 0.0 + up_perturb: 0.0 + eval_height: 512 + eval_width: 512 + eval_elevation_deg: ${data.default_elevation_deg} + eval_camera_distance: ${data.default_camera_distance} + eval_fovy_deg: ${data.default_fovy_deg} + n_val_views: 4 + n_test_views: 120 + +system_type: "zero123-simple-system" +system: + geometry_type: "implicit-volume" + geometry: + radius: 1.0 + normal_type: analytic + + # use Magic3D density initialization + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + pos_encoding_config: + otype: ProgressiveBandHashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + start_level: 8 # resolution ~200 + start_step: 2000 + update_steps: 300 + mlp_network_config: + otype: "VanillaMLP" + activation: "ReLU" + output_activation: "none" + n_neurons: 64 + n_hidden_layers: 2 + + material_type: "no-material" + material: + requires_normal: true + + background_type: "solid-color-background" + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 256 + return_comp_normal: true + + guidance_type: "zero123-unified-guidance" + guidance: + guidance_type: "sds" + # guidance_type: "vsd" # switch to this line to use vsd + pretrained_model_name_or_path: "bennyguo/zero123-diffusers" + guidance_scale: 5.0 + # empirical timestep annealing + min_step_percent: 0.1 + max_step_percent: [5000, 0.9, 0.5, 5001] + + cond_image_path: ${data.image_path} + cond_elevation_deg: ${data.default_elevation_deg} + cond_azimuth_deg: ${data.default_azimuth_deg} + cond_camera_distance: ${data.default_camera_distance} + + # debug use, may slowdown training + return_rgb_1step_orig: true + + # for vsd + vsd_phi_model_name_or_path: null + vsd_use_camera_condition: false + use_img_loss: false + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sd: 1.0 + lambda_train_phi: 1.0 # for vsd + lambda_orient: 0. + lambda_normal_smoothness_2d: 1000. + lambda_sparsity: 0. + lambda_opaque: 0. + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-8 + params: + geometry.encoding: + lr: 0.01 + geometry.density_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + # for vsd + guidance: + lr: 0.0001 + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 1 + val_check_interval: 100 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/fantasia3d-texture.yaml b/configs/fantasia3d-texture.yaml new file mode 100644 index 0000000..3c48dd4 --- /dev/null +++ b/configs/fantasia3d-texture.yaml @@ -0,0 +1,129 @@ +name: "fantasia3d-texture" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: 1 + width: 512 + height: 512 + camera_distance_range: [3, 3] + fovy_range: [25, 45] + camera_perturb: 0. + center_perturb: 0. + up_perturb: 0. + elevation_range: [-10, 45] + azimuth_range: [-180, 180] + batch_uniform_azimuth: true + eval_camera_distance: 3. + eval_fovy_deg: 45. + +system_type: "fantasia3d-system" +system: + # do texture training + texture: true + + # If using geometry from previous training + geometry_convert_from: ??? + geometry_convert_inherit_texture: false + geometry_type: "tetrahedra-sdf-grid" + geometry: + radius: 1.0 # consistent with coarse + isosurface_resolution: 128 + isosurface_deformable_grid: true + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.4472692374403782 # max resolution 4096 + n_feature_dims: 8 # albedo3 + roughness1 + metallic1 + bump3 + fix_geometry: true + + # If using custom mesh + # geometry_type: "custom-mesh" + # geometry: + # shape_init: ??? + # radius: 1.0 # consistent with coarse + # pos_encoding_config: + # otype: HashGrid + # n_levels: 16 + # n_features_per_level: 2 + # log2_hashmap_size: 19 + # base_resolution: 16 + # per_level_scale: 1.4472692374403782 # max resolution 4096 + # n_feature_dims: 8 # albedo3 + roughness1 + metallic1 + bump3 + + material_type: "pbr-material" + material: + material_activation: sigmoid + environment_texture: "load/lights/mud_road_puresky_1k.hdr" + environment_scale: 2.0 + min_metallic: 0.0 + max_metallic: 0.9 + min_roughness: 0.08 + max_roughness: 0.9 + use_bump: true + + background_type: "solid-color-background" + + renderer_type: "nvdiff-rasterizer" + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 100 + weighting_strategy: sds + min_step_percent: 0.02 + max_step_percent: 0.50 + + # If using controlnet guidance: +# prompt_processor_type: "stable-diffusion-prompt-processor" +# prompt_processor: +# pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + +# guidance_type: "stable-diffusion-controlnet-guidance" +# guidance: +# control_type: "normal" +# min_step_percent: 0.02 +# max_step_percent: 0.50 +# condition_scale: 1.0 +# guidance_scale: 100 +# use_sds: true + + + loggers: + wandb: + enable: false + project: "threestudio" + + loss: + lambda_sds: 1. + lambda_normal_consistency: 0. + + optimizer: + name: AdamW + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + +trainer: + max_steps: 5000 + log_every_n_steps: 1 + num_sanity_val_steps: 1 + val_check_interval: 500 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/fantasia3d.yaml b/configs/fantasia3d.yaml new file mode 100644 index 0000000..858b4b9 --- /dev/null +++ b/configs/fantasia3d.yaml @@ -0,0 +1,98 @@ +name: "fantasia3d" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: 1 + width: 512 + height: 512 + camera_distance_range: [3, 3] + fovy_range: [25, 45] + camera_perturb: 0. + center_perturb: 0. + up_perturb: 0. + elevation_range: [-10, 45] + azimuth_range: [-180, 180] + batch_uniform_azimuth: true + eval_camera_distance: 3. + eval_fovy_deg: 45. + +system_type: "fantasia3d-system" +system: + latent_steps: 1000 + geometry_type: "implicit-sdf" + geometry: + radius: 1.0 + n_feature_dims: 0 + isosurface_resolution: 128 + isosurface_deformable_grid: true + isosurface_coarse_to_fine: false + + # initialize SDF by optimization + shape_init: sphere + shape_init_params: 0.5 + + # or you can initialize SDF using a guide mesh + # shape_init: mesh:load/shapes/human.obj + # shape_init_params: 0.9 + # shape_init_mesh_up: +y + # shape_init_mesh_front: +z + + # an alternative initialization implementation: + # you can initialize SDF to sphere/ellipsoid by adding a bias value + # which leads to more smooth initialized shape + # sdf_bias: sphere + # sdf_bias_params: 0.5 + # DO NOT use the two initialization methods together + + material_type: "no-material" # unused + material: + n_output_dims: 0 + + background_type: "solid-color-background" # unused + + renderer_type: "nvdiff-rasterizer" + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 100. + max_step_percent: 0.5 + weighting_strategy: fantasia3d + + loggers: + wandb: + enable: false + project: 'threestudio' + name: None + + loss: + lambda_sds: 1. + lambda_normal_consistency: 0. + + optimizer: + name: AdamW + args: + lr: 0.001 + betas: [0.9, 0.99] + eps: 1.e-15 + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 1 + val_check_interval: 500 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/gradio/dreamfusion-if.yaml b/configs/gradio/dreamfusion-if.yaml new file mode 100644 index 0000000..ac88a32 --- /dev/null +++ b/configs/gradio/dreamfusion-if.yaml @@ -0,0 +1,119 @@ +name: "dreamfusion-if" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs-gradio" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: 1 + width: 64 + height: 64 + camera_distance_range: [1.5, 2.0] + fovy_range: [40, 70] + elevation_range: [-10, 90] + light_sample_strategy: "dreamfusion" + eval_camera_distance: 2.0 + eval_fovy_deg: 70. + +system_type: "dreamfusion-system" +system: + geometry_type: "implicit-volume" + geometry: + radius: 2.0 + normal_type: "analytic" + + # the density initialization proposed in the DreamFusion paper + # does not work very well + # density_bias: "blob_dreamfusion" + # density_activation: exp + # density_blob_scale: 5. + # density_blob_std: 0.2 + + # use Magic3D density initialization instead + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + # coarse to fine hash grid encoding + # to ensure smooth analytic normals + pos_encoding_config: + otype: ProgressiveBandHashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.381912879967776 # max resolution 2048 + start_level: 10 # resolution ~300 + start_step: 2000 + update_steps: 400 + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 2001 + albedo_activation: scale_-11_01 + + background_type: "neural-environment-map-background" + background: + color_activation: scale_-11_01 + random_aug: true + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + + prompt_processor_type: "deep-floyd-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + prompt: ??? + + guidance_type: "deep-floyd-guidance" + guidance: + pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + guidance_scale: 20. + weighting_strategy: sds + min_step_percent: 0.02 + max_step_percent: 0.98 + + exporter_type: "mesh-exporter" + exporter: + fmt: obj + save_uv: false + context_type: cuda + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 1. + lambda_orient: [0, 10., 1000., 5000] + lambda_sparsity: 1. + lambda_opaque: 0.0 + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry: + lr: 0.01 + background: + lr: 0.001 + +trainer: + max_steps: 5000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 100 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: false + save_top_k: -1 + every_n_train_steps: 0 # do not save checkpoints during training diff --git a/configs/gradio/dreamfusion-sd.yaml b/configs/gradio/dreamfusion-sd.yaml new file mode 100644 index 0000000..afd70f3 --- /dev/null +++ b/configs/gradio/dreamfusion-sd.yaml @@ -0,0 +1,121 @@ +name: "dreamfusion-sd" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs-gradio" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: 1 + width: 64 + height: 64 + camera_distance_range: [1.5, 2.0] + fovy_range: [40, 70] + elevation_range: [-10, 45] + light_sample_strategy: "dreamfusion" + eval_camera_distance: 2.0 + eval_fovy_deg: 70. + +system_type: "dreamfusion-system" +system: + geometry_type: "implicit-volume" + geometry: + radius: 2.0 + normal_type: "analytic" + + # the density initialization proposed in the DreamFusion paper + # does not work very well + # density_bias: "blob_dreamfusion" + # density_activation: exp + # density_blob_scale: 5. + # density_blob_std: 0.2 + + # use Magic3D density initialization instead + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + # coarse to fine hash grid encoding + # to ensure smooth analytic normals + pos_encoding_config: + otype: ProgressiveBandHashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.381912879967776 # max resolution 2048 + start_level: 10 # resolution ~300 + start_step: 2000 + update_steps: 400 + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 2001 + albedo_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + random_aug: true + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 100. + weighting_strategy: sds + min_step_percent: 0.02 + max_step_percent: 0.98 + grad_clip: [0, 0.5, 2.0, 5000] + + exporter_type: "mesh-exporter" + exporter: + fmt: obj + save_uv: false + context_type: cuda + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 1. + lambda_orient: [0, 10., 1000., 5000] + lambda_sparsity: 1. + lambda_opaque: 0. + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry: + lr: 0.01 + background: + lr: 0.001 + +trainer: + max_steps: 5000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 100 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: false + save_top_k: -1 + every_n_train_steps: 0 # do not save checkpoints during training diff --git a/configs/gradio/fantasia3d.yaml b/configs/gradio/fantasia3d.yaml new file mode 100644 index 0000000..6b962f8 --- /dev/null +++ b/configs/gradio/fantasia3d.yaml @@ -0,0 +1,107 @@ +name: "fantasia3d" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs-gradio" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: 1 + width: 512 + height: 512 + camera_distance_range: [3, 3] + fovy_range: [25, 45] + camera_perturb: 0. + center_perturb: 0. + up_perturb: 0. + elevation_range: [-10, 45] + azimuth_range: [-180, 180] + batch_uniform_azimuth: true + eval_camera_distance: 3. + eval_fovy_deg: 45. + +system_type: "fantasia3d-system" +system: + latent_steps: 1000 + geometry_type: "implicit-sdf" + geometry: + radius: 1.0 + n_feature_dims: 0 + isosurface_resolution: 128 + isosurface_deformable_grid: true + isosurface_coarse_to_fine: false + + # initialize SDF by optimization + shape_init: sphere + shape_init_params: 0.5 + + # or you can initialize SDF using a guide mesh + # shape_init: mesh:load/shapes/human.obj + # shape_init_params: 0.9 + # shape_init_mesh_up: +y + # shape_init_mesh_front: +z + + # an alternative initialization implementation: + # you can initialize SDF to sphere/ellipsoid by adding a bias value + # which leads to more smooth initialized shape + # sdf_bias: sphere + # sdf_bias_params: 0.5 + # DO NOT use the two initialization methods together + + material_type: "no-material" # unused + material: + n_output_dims: 0 + + background_type: "solid-color-background" # unused + + renderer_type: "nvdiff-rasterizer" + renderer: + context_type: cuda + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 100. + max_step_percent: 0.5 + weighting_strategy: fantasia3d + + exporter_type: "mesh-exporter" + exporter: + fmt: obj + save_uv: false + save_texture: false + context_type: cuda + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 1. + lambda_normal_consistency: 0. + + optimizer: + name: AdamW + args: + lr: 0.001 + betas: [0.9, 0.99] + eps: 1.e-15 + +trainer: + max_steps: 5000 + log_every_n_steps: 1 + num_sanity_val_steps: 1 + val_check_interval: 200 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: false + save_top_k: -1 + every_n_train_steps: 0 # do not save checkpoints during training diff --git a/configs/gradio/latentnerf.yaml b/configs/gradio/latentnerf.yaml new file mode 100644 index 0000000..b9d7087 --- /dev/null +++ b/configs/gradio/latentnerf.yaml @@ -0,0 +1,103 @@ +name: "latentnerf" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs-gradio" +seed: 0 + +data_type: "random-camera-datamodule" +data: + elevation_range: [-10, 45] + +system_type: "latentnerf-system" +system: + geometry_type: "implicit-volume" + geometry: + n_feature_dims: 4 + normal_type: null + + density_bias: "blob_dreamfusion" + density_activation: trunc_exp + density_blob_scale: 5. + density_blob_std: 0.2 + + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.381912879967776 # max resolution 2048 + + material_type: "no-material" + material: + n_output_dims: 4 + color_activation: none + + background_type: "neural-environment-map-background" + background: + n_output_dims: 4 + color_activation: none + + renderer_type: "nerf-volume-renderer" + renderer: + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 100. + weighting_strategy: sds + grad_clip: [0, 2.0, 8.0, 5000] + + exporter_type: "dummy-exporter" + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 1. + lambda_sparsity: 5.e-4 + lambda_opaque: 0.0 + lambda_orient: 0.0 + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + scheduler: + name: SequentialLR + interval: step + warmup_steps: 100 + milestones: + - ${system.scheduler.warmup_steps} + schedulers: + - name: LinearLR # linear warm-up in the first system.warmup_steps steps + args: + start_factor: 0.1 + end_factor: 1.0 + total_iters: ${system.scheduler.warmup_steps} + - name: ExponentialLR + args: + gamma: ${calc_exp_lr_decay_rate:0.1,${sub:${trainer.max_steps},${system.scheduler.warmup_steps}}} + +trainer: + max_steps: 5000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: false + save_top_k: -1 + every_n_train_steps: 0 # do not save checkpoints during training diff --git a/configs/gradio/sjc.yaml b/configs/gradio/sjc.yaml new file mode 100644 index 0000000..8471270 --- /dev/null +++ b/configs/gradio/sjc.yaml @@ -0,0 +1,96 @@ +name: sjc +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs-gradio" +seed: 0 + +data_type: random-camera-datamodule +data: + camera_distance_range: [1.50, 1.50] + elevation_range: [-10, 45] + camera_perturb: 0.0 + center_perturb: 0.0 + up_perturb: 0.0 + light_position_perturb: 0.0 + eval_elevation_deg: 20.0 + +system_type: sjc-system +system: + subpixel_rendering: false + + geometry_type: volume-grid + geometry: + normal_type: null + grid_size: [100, 100, 100] + density_bias: -1.0 + n_feature_dims: 4 + + material_type: no-material + material: + n_output_dims: 4 + color_activation: none + + background_type: textured-background + background: + n_output_dims: 4 + color_activation: none + height: 4 + width: 4 + + renderer_type: nerf-volume-renderer + renderer: + num_samples_per_ray: 512 + grid_prune: false + + prompt_processor_type: stable-diffusion-prompt-processor + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + view_dependent_prompt_front: true + + guidance_type: stable-diffusion-guidance + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 100. + use_sjc: true + var_red: true + min_step_percent: 0.01 + max_step_percent: 0.97 + grad_clip: [0, 2.0, 8.0, 5000] + + exporter_type: "dummy-exporter" + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 1. + center_ratio: 0.78125 # = 50 / 64 + lambda_depth: 0 # or try 10 + lambda_emptiness: [5000, 1.e+4, 2.e+5, 5001] + emptiness_scale: 10 + + optimizer: + name: Adamax + args: + lr: 0.05 + params: + geometry: + lr: 0.05 + background: + lr: 0.0001 # maybe 0.001/0.01 is better + +trainer: + max_steps: 5000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: false + save_top_k: -1 + every_n_train_steps: 0 # do not save checkpoints during training diff --git a/configs/gradio/textmesh-if.yaml b/configs/gradio/textmesh-if.yaml new file mode 100644 index 0000000..7ea69a7 --- /dev/null +++ b/configs/gradio/textmesh-if.yaml @@ -0,0 +1,112 @@ +name: "textmesh-if" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs-gradio" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: 1 + width: 64 + height: 64 + camera_distance_range: [1.5, 2.0] + fovy_range: [40, 70] + elevation_range: [-10, 90] + light_sample_strategy: "dreamfusion" + eval_camera_distance: 2.0 + eval_fovy_deg: 70. + +system_type: "textmesh-system" +system: + geometry_type: "implicit-sdf" + geometry: + radius: 2.0 + normal_type: finite_difference + # progressive eps from Neuralangelo + finite_difference_normal_eps: progressive + + sdf_bias: sphere + sdf_bias_params: 0.5 + + # coarse to fine hash grid encoding + pos_encoding_config: + otype: ProgressiveBandHashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.381912879967776 # max resolution 2048 + start_level: 10 # resolution ~300 + start_step: 2000 + update_steps: 400 + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 2001 + albedo_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + random_aug: true + + renderer_type: "neus-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + cos_anneal_end_steps: ${trainer.max_steps} + eval_chunk_size: 8192 + + prompt_processor_type: "deep-floyd-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + prompt: ??? + + guidance_type: "deep-floyd-guidance" + guidance: + pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + guidance_scale: 20. + weighting_strategy: sds + min_step_percent: 0.02 + max_step_percent: 0.98 + + exporter_type: "mesh-exporter" + exporter: + fmt: obj + save_uv: false + context_type: cuda + + loss: + lambda_sds: 1. + lambda_orient: 0.0 + lambda_sparsity: 0.0 + lambda_opaque: 0.0 + lambda_eikonal: 1000. + optimizer: + name: Adam + args: + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry.encoding: + lr: 0.01 + geometry.sdf_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + background: + lr: 0.001 + renderer: + lr: 0.001 + +trainer: + max_steps: 5000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 100 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: false + save_top_k: -1 + every_n_train_steps: 0 # do not save checkpoints during training diff --git a/configs/hifa.yaml b/configs/hifa.yaml new file mode 100644 index 0000000..a5c32ab --- /dev/null +++ b/configs/hifa.yaml @@ -0,0 +1,111 @@ +name: "hifa" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: 1 + width: 512 + height: 512 + camera_distance_range: [1.0, 1.5] + fovy_range: [40, 70] + elevation_range: [-10, 45] + camera_perturb: 0. + center_perturb: 0. + up_perturb: 0. + eval_camera_distance: 1.5 + eval_fovy_deg: 70. + +system_type: "dreamfusion-system" +system: + geometry_type: "implicit-volume" + geometry: + radius: 1.0 + normal_type: null + + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + + material_type: "no-material" + material: + n_output_dims: 3 + color_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + random_aug: true + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 100. + weighting_strategy: sds + min_step_percent: 0.3 + max_step_percent: 0.98 + sqrt_anneal: true + use_img_loss: true + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 1. + lambda_sds_img: 0.01 + lambda_orient: 0. + lambda_sparsity: 1. + lambda_opaque: 0. + lambda_z_variance: 100 + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry.encoding: + lr: 0.01 + geometry.density_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + background: + lr: 0.001 + +trainer: + max_steps: 25000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/instructnerf2nerf.yaml b/configs/instructnerf2nerf.yaml new file mode 100644 index 0000000..2339ccc --- /dev/null +++ b/configs/instructnerf2nerf.yaml @@ -0,0 +1,99 @@ +name: "instructnerf2nerf" +tag: "${basename:${data.dataroot}}_${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "multiview-camera-datamodule" +data: + train_downsample_resolution: 2 + eval_downsample_resolution: 2 + dataroot: ??? + +system_type: "instructnerf2nerf-system" +system: + start_editing_step: 600 + per_editing_step: 10 + + geometry_type: "implicit-volume" + geometry: + radius: 1. + normal_type: analytic + + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.4472692374403782 # max resolution 4096 + + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 9999999 + albedo_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + random_aug: false + + renderer_type: "patch-renderer" + renderer: + base_renderer_type: "nerf-volume-renderer" + base_renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 384 + patch_size: 128 + + guidance_type: "stable-diffusion-instructpix2pix-guidance" + guidance: + min_step_percent: 0.02 + max_step_percent: 0.98 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + prompt: "Turn him into Elon Musk" + + loggers: + wandb: + enable: false + project: 'threestudio' + + loss: + lambda_sds: 0. + lambda_orient: [0, 10.0, 1000., 5000.0] + lambda_sparsity: 1.0 + lambda_opaque: 1.0 + lambda_l1: 10. + lambda_p: 10. + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry: + lr: 0.01 + background: + lr: 0.001 + +trainer: + max_steps: 20000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 600 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/latentnerf-refine.yaml b/configs/latentnerf-refine.yaml new file mode 100644 index 0000000..2f2f5df --- /dev/null +++ b/configs/latentnerf-refine.yaml @@ -0,0 +1,90 @@ +name: "latentnerf-refine" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + elevation_range: [-10, 45] + +system_type: "latentnerf-system" +system: + refinement: true + weights: ??? + weights_ignore_modules: ["material", "background"] + + geometry_type: "implicit-volume" + geometry: + n_feature_dims: 4 + normal_type: null + + density_bias: "blob_dreamfusion" + density_activation: trunc_exp + density_blob_scale: 5. + density_blob_std: 0.2 + + material_type: "sd-latent-adapter-material" + + background_type: "neural-environment-map-background" + + renderer_type: "nerf-volume-renderer" + renderer: + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 100. + weighting_strategy: sds + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 1. + lambda_sparsity: 5.e-4 + lambda_opaque: 0.0 + lambda_orient: 0.0 + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + scheduler: + name: SequentialLR + interval: step + warmup_steps: 100 + milestones: + - ${system.scheduler.warmup_steps} + schedulers: + - name: LinearLR # linear warm-up in the first system.warmup_steps steps + args: + start_factor: 0.1 + end_factor: 1.0 + total_iters: ${system.scheduler.warmup_steps} + - name: ExponentialLR + args: + gamma: ${calc_exp_lr_decay_rate:0.1,${sub:${trainer.max_steps},${system.scheduler.warmup_steps}}} + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 1 + val_check_interval: 200 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/latentnerf.yaml b/configs/latentnerf.yaml new file mode 100644 index 0000000..ecafb0f --- /dev/null +++ b/configs/latentnerf.yaml @@ -0,0 +1,92 @@ +name: "latentnerf" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + elevation_range: [-10, 45] + +system_type: "latentnerf-system" +system: + geometry_type: "implicit-volume" + geometry: + n_feature_dims: 4 + normal_type: null + + density_bias: "blob_dreamfusion" + density_activation: trunc_exp + density_blob_scale: 5. + density_blob_std: 0.2 + + material_type: "no-material" + material: + n_output_dims: 4 + color_activation: none + + background_type: "neural-environment-map-background" + background: + n_output_dims: 4 + color_activation: none + + renderer_type: "nerf-volume-renderer" + renderer: + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 100. + weighting_strategy: sds + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 1. + lambda_sparsity: 5.e-4 + lambda_opaque: 0.0 + lambda_orient: 0.0 + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + scheduler: + name: SequentialLR + interval: step + warmup_steps: 100 + milestones: + - ${system.scheduler.warmup_steps} + schedulers: + - name: LinearLR # linear warm-up in the first system.warmup_steps steps + args: + start_factor: 0.1 + end_factor: 1.0 + total_iters: ${system.scheduler.warmup_steps} + - name: ExponentialLR + args: + gamma: ${calc_exp_lr_decay_rate:0.1,${sub:${trainer.max_steps},${system.scheduler.warmup_steps}}} + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/magic123-coarse-sd.yaml b/configs/magic123-coarse-sd.yaml new file mode 100644 index 0000000..5fd06fa --- /dev/null +++ b/configs/magic123-coarse-sd.yaml @@ -0,0 +1,147 @@ +name: "magic123-coarse-sd" +tag: "${rmspace:${basename:${data.image_path}},_}-${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "single-image-datamodule" +data: + image_path: ??? + height: 128 + width: 128 + default_elevation_deg: 0.0 + default_azimuth_deg: 0.0 + default_camera_distance: 2.5 + default_fovy_deg: 40.0 + random_camera: + batch_size: 1 + height: 128 + width: 128 + elevation_range: [-45, 45] + azimuth_range: [-180, 180] + camera_distance_range: [2.5, 2.5] + fovy_range: [40.0, 40.0] + camera_perturb: 0.0 + center_perturb: 0.0 + up_perturb: 0.0 + eval_height: 512 + eval_width: 512 + eval_elevation_deg: ${data.default_elevation_deg} + eval_camera_distance: ${data.default_camera_distance} + eval_fovy_deg: ${data.default_fovy_deg} + n_val_views: 4 + n_test_views: 120 + +system_type: "magic123-system" +system: + geometry_type: "implicit-volume" + geometry: + radius: 1.0 + normal_type: analytic + + # use Magic3D density initialization + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + # coarse to fine hash grid encoding + # to ensure smooth analytic normals + pos_encoding_config: + otype: ProgressiveBandHashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + start_level: 8 # resolution ~200 + start_step: 2000 + update_steps: 300 + mlp_network_config: + otype: "VanillaMLP" + activation: "ReLU" + output_activation: "none" + n_neurons: 64 + n_hidden_layers: 2 + + material_type: "no-material" + material: + requires_normal: true + + background_type: "solid-color-background" + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + estimator: occgrid + num_samples_per_ray: 256 + return_comp_normal: true + + guidance_3d_type: "zero123-unified-guidance" + guidance_3d: + guidance_type: "sds" + guidance_scale: 5.0 + min_step_percent: 0.2 + max_step_percent: 0.6 + + cond_image_path: ${data.image_path} + cond_elevation_deg: ${data.default_elevation_deg} + cond_azimuth_deg: ${data.default_azimuth_deg} + cond_camera_distance: ${data.default_camera_distance} + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + prompt: ??? + + guidance_type: "stable-diffusion-unified-guidance" + guidance: + guidance_type: "sds" + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + guidance_scale: 100. + min_step_percent: 0.2 + max_step_percent: 0.6 + use_img_loss: false + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_rgb: 1000. + lambda_mask: 100. + lambda_sd: 0.025 + lambda_3d_sd: 1. + lambda_sd_img: 0. + lambda_orient: 0. + lambda_normal_smoothness_2d: 1000. + lambda_sparsity: 0. + lambda_opaque: 0. + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-8 + params: + geometry.encoding: + lr: 0.01 + geometry.density_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 100 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/magic123-hifa-coarse-sd.yaml b/configs/magic123-hifa-coarse-sd.yaml new file mode 100644 index 0000000..fb96eab --- /dev/null +++ b/configs/magic123-hifa-coarse-sd.yaml @@ -0,0 +1,148 @@ +name: "magic123-hifa-coarse-sd" +tag: "${rmspace:${basename:${data.image_path}},_}-${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "single-image-datamodule" +data: + image_path: ??? + height: 128 + width: 128 + default_elevation_deg: 0.0 + default_azimuth_deg: 0.0 + default_camera_distance: 2.5 + default_fovy_deg: 40.0 + random_camera: + batch_size: 1 + height: 128 + width: 128 + elevation_range: [-45, 45] + azimuth_range: [-180, 180] + camera_distance_range: [2.5, 2.5] + fovy_range: [40.0, 40.0] + camera_perturb: 0.0 + center_perturb: 0.0 + up_perturb: 0.0 + eval_height: 512 + eval_width: 512 + eval_elevation_deg: ${data.default_elevation_deg} + eval_camera_distance: ${data.default_camera_distance} + eval_fovy_deg: ${data.default_fovy_deg} + n_val_views: 4 + n_test_views: 120 + +system_type: "magic123-system" +system: + geometry_type: "implicit-volume" + geometry: + radius: 1.0 + normal_type: analytic + + # use Magic3D density initialization + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + # coarse to fine hash grid encoding + # to ensure smooth analytic normals + pos_encoding_config: + otype: ProgressiveBandHashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + start_level: 8 # resolution ~200 + start_step: 2000 + update_steps: 300 + mlp_network_config: + otype: "VanillaMLP" + activation: "ReLU" + output_activation: "none" + n_neurons: 64 + n_hidden_layers: 2 + + material_type: "no-material" + material: + requires_normal: true + + background_type: "solid-color-background" + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + estimator: occgrid + num_samples_per_ray: 256 + return_comp_normal: true + + guidance_3d_type: "zero123-unified-guidance" + guidance_3d: + guidance_type: "sds" + guidance_scale: 5.0 + min_step_percent: 0.2 + max_step_percent: 0.6 + + cond_image_path: ${data.image_path} + cond_elevation_deg: ${data.default_elevation_deg} + cond_azimuth_deg: ${data.default_azimuth_deg} + cond_camera_distance: ${data.default_camera_distance} + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + prompt: ??? + + guidance_type: "stable-diffusion-unified-guidance" + guidance: + guidance_type: "sds" + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + guidance_scale: 100. + min_step_percent: 0.2 + max_step_percent: 0.6 + use_img_loss: true + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_rgb: 1000. + lambda_mask: 100. + lambda_sd: 0.025 + lambda_3d_sd: 1. + lambda_sd_img: 0.00025 + lambda_orient: 0. + lambda_normal_smoothness_2d: 1000. + lambda_sparsity: 0. + lambda_opaque: 0. + lambda_z_variance: 100. + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-8 + params: + geometry.encoding: + lr: 0.01 + geometry.density_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 100 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/magic123-hifa-refine-sd.yaml b/configs/magic123-hifa-refine-sd.yaml new file mode 100644 index 0000000..2e41f34 --- /dev/null +++ b/configs/magic123-hifa-refine-sd.yaml @@ -0,0 +1,126 @@ +name: "magic123-hifa-refine-sd" +tag: "${rmspace:${basename:${data.image_path}},_}-${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "single-image-datamodule" +data: + image_path: ??? + height: 512 + width: 512 + default_elevation_deg: 0.0 + default_azimuth_deg: 0.0 + default_camera_distance: 2.5 + default_fovy_deg: 40.0 + random_camera: + batch_size: 1 + height: 512 + width: 512 + elevation_range: [-45, 45] + azimuth_range: [-180, 180] + camera_distance_range: [2.5, 2.5] + fovy_range: [40.0, 40.0] + camera_perturb: 0.0 + center_perturb: 0.0 + up_perturb: 0.0 + eval_height: 512 + eval_width: 512 + eval_elevation_deg: ${data.default_elevation_deg} + eval_camera_distance: ${data.default_camera_distance} + eval_fovy_deg: ${data.default_fovy_deg} + n_val_views: 4 + n_test_views: 120 + +system_type: "magic123-system" +system: + refinement: true + geometry_convert_from: ??? + geometry_convert_inherit_texture: true + geometry_type: "tetrahedra-sdf-grid" + geometry: + radius: 1.0 # consistent with coarse + isosurface_resolution: 128 + isosurface_deformable_grid: true + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + mlp_network_config: + otype: "VanillaMLP" + activation: "ReLU" + output_activation: "none" + n_neurons: 64 + n_hidden_layers: 2 + fix_geometry: false # optimize grid sdf and deformation + + material_type: "no-material" + + background_type: "solid-color-background" + + renderer_type: "nvdiff-rasterizer" + renderer: + context_type: gl + + guidance_3d_type: "zero123-unified-guidance" + guidance_3d: + guidance_type: "sds" + guidance_scale: 5.0 + min_step_percent: 0.2 + max_step_percent: 0.6 + + cond_image_path: ${data.image_path} + cond_elevation_deg: ${data.default_elevation_deg} + cond_azimuth_deg: ${data.default_azimuth_deg} + cond_camera_distance: ${data.default_camera_distance} + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + prompt: ??? + + guidance_type: "stable-diffusion-unified-guidance" + guidance: + guidance_type: "sds" + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + guidance_scale: 100. + min_step_percent: 0.2 + max_step_percent: 0.6 + use_img_loss: true + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_rgb: 1000. + lambda_mask: 100. + lambda_sd: 0.025 + lambda_3d_sd: 1. + lambda_sd_img: 0.00025 + lambda_normal_consistency: 1000. + lambda_laplacian_smoothness: 0. + + optimizer: + name: Adam + args: + lr: 0.001 + betas: [0.9, 0.99] + eps: 1.e-8 + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 1 + val_check_interval: 100 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/magic123-refine-sd.yaml b/configs/magic123-refine-sd.yaml new file mode 100644 index 0000000..984be46 --- /dev/null +++ b/configs/magic123-refine-sd.yaml @@ -0,0 +1,126 @@ +name: "magic123-refine-sd" +tag: "${rmspace:${basename:${data.image_path}},_}-${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "single-image-datamodule" +data: + image_path: ??? + height: 512 + width: 512 + default_elevation_deg: 0.0 + default_azimuth_deg: 0.0 + default_camera_distance: 2.5 + default_fovy_deg: 40.0 + random_camera: + batch_size: 1 + height: 512 + width: 512 + elevation_range: [-45, 45] + azimuth_range: [-180, 180] + camera_distance_range: [2.5, 2.5] + fovy_range: [40.0, 40.0] + camera_perturb: 0.0 + center_perturb: 0.0 + up_perturb: 0.0 + eval_height: 512 + eval_width: 512 + eval_elevation_deg: ${data.default_elevation_deg} + eval_camera_distance: ${data.default_camera_distance} + eval_fovy_deg: ${data.default_fovy_deg} + n_val_views: 4 + n_test_views: 120 + +system_type: "magic123-system" +system: + refinement: true + geometry_convert_from: ??? + geometry_convert_inherit_texture: true + geometry_type: "tetrahedra-sdf-grid" + geometry: + radius: 1.0 # consistent with coarse + isosurface_resolution: 128 + isosurface_deformable_grid: true + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + mlp_network_config: + otype: "VanillaMLP" + activation: "ReLU" + output_activation: "none" + n_neurons: 64 + n_hidden_layers: 2 + fix_geometry: false # optimize grid sdf and deformation + + material_type: "no-material" + + background_type: "solid-color-background" + + renderer_type: "nvdiff-rasterizer" + renderer: + context_type: gl + + guidance_3d_type: "zero123-unified-guidance" + guidance_3d: + guidance_type: "sds" + guidance_scale: 5.0 + min_step_percent: 0.2 + max_step_percent: 0.6 + + cond_image_path: ${data.image_path} + cond_elevation_deg: ${data.default_elevation_deg} + cond_azimuth_deg: ${data.default_azimuth_deg} + cond_camera_distance: ${data.default_camera_distance} + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + prompt: ??? + + guidance_type: "stable-diffusion-unified-guidance" + guidance: + guidance_type: "sds" + pretrained_model_name_or_path: "runwayml/stable-diffusion-v1-5" + guidance_scale: 100. + min_step_percent: 0.2 + max_step_percent: 0.6 + use_img_loss: false + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_rgb: 1000. + lambda_mask: 100. + lambda_sd: 0.025 + lambda_3d_sd: 1. + lambda_sd_img: 0. + lambda_normal_consistency: 1000. + lambda_laplacian_smoothness: 0. + + optimizer: + name: Adam + args: + lr: 0.001 + betas: [0.9, 0.99] + eps: 1.e-8 + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 1 + val_check_interval: 100 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/magic3d-coarse-if.yaml b/configs/magic3d-coarse-if.yaml new file mode 100644 index 0000000..1660191 --- /dev/null +++ b/configs/magic3d-coarse-if.yaml @@ -0,0 +1,95 @@ +name: "magic3d-coarse-if" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + width: 64 + height: 64 + camera_distance_range: [1.5, 2.0] + light_sample_strategy: "magic3d" + eval_camera_distance: 2.0 + eval_fovy_deg: 70. + +system_type: "magic3d-system" +system: + geometry_type: "implicit-volume" + geometry: + radius: 2. + normal_type: analytic + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.4472692374403782 # max resolution 4096 + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + isosurface_resolution: 128 + isosurface_threshold: auto + isosurface_coarse_to_fine: true + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 2001 + soft_shading: true + + background_type: "neural-environment-map-background" + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + + prompt_processor_type: "deep-floyd-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + prompt: ??? + + guidance_type: "deep-floyd-guidance" + guidance: + pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + weighting_strategy: uniform + guidance_scale: 20. + min_step_percent: 0.02 + max_step_percent: 0.98 + + loggers: + wandb: + enable: false + project: 'threestudio' + name: None + + loss: + lambda_sds: 1. + lambda_orient: [0, 10., 1000., 5000] + lambda_sparsity: 1. + lambda_opaque: 0. + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry: + lr: 0.01 + background: + lr: 0.001 + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/magic3d-coarse-sd.yaml b/configs/magic3d-coarse-sd.yaml new file mode 100644 index 0000000..566acbc --- /dev/null +++ b/configs/magic3d-coarse-sd.yaml @@ -0,0 +1,97 @@ +name: "magic3d-coarse-sd" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + width: 64 + height: 64 + camera_distance_range: [1.5, 2.0] + elevation_range: [-10, 45] + light_sample_strategy: "magic3d" + eval_camera_distance: 2.0 + eval_fovy_deg: 70. + +system_type: "magic3d-system" +system: + geometry_type: "implicit-volume" + geometry: + radius: 2. + normal_type: analytic + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.4472692374403782 # max resolution 4096 + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + isosurface_resolution: 128 + isosurface_threshold: auto + isosurface_coarse_to_fine: true + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 2001 + soft_shading: true + + background_type: "neural-environment-map-background" + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + weighting_strategy: uniform + guidance_scale: 100. + min_step_percent: 0.02 + max_step_percent: 0.98 + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 1. + lambda_orient: [0, 10., 1000., 5000] + lambda_sparsity: 1. + lambda_opaque: 0. + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry: + lr: 0.01 + background: + lr: 0.001 + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/magic3d-refine-sd.yaml b/configs/magic3d-refine-sd.yaml new file mode 100644 index 0000000..c60038a --- /dev/null +++ b/configs/magic3d-refine-sd.yaml @@ -0,0 +1,88 @@ +name: "magic3d-refine-sd" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + width: 512 + height: 512 + camera_distance_range: [1.5, 2.0] + elevation_range: [-10, 45] + light_sample_strategy: "magic3d" + fovy_range: [30, 45] + eval_camera_distance: 2.0 + eval_fovy_deg: 70. + +system_type: "magic3d-system" +system: + refinement: true + geometry_convert_from: ??? + geometry_convert_inherit_texture: true + geometry_type: "tetrahedra-sdf-grid" + geometry: + radius: 2.0 # consistent with coarse + isosurface_resolution: 128 + isosurface_deformable_grid: true + pos_encoding_config: # consistent with coarse, no progressive band + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.4472692374403782 # max resolution 4096 + fix_geometry: false # optimize grid sdf and deformation + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 0 + soft_shading: true + + background_type: "neural-environment-map-background" + + renderer_type: "nvdiff-rasterizer" + renderer: + context_type: gl + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + weighting_strategy: sds + guidance_scale: 100. + min_step_percent: 0.02 + max_step_percent: 0.5 + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 1. + lambda_normal_consistency: 10000. + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + +trainer: + max_steps: 5000 + log_every_n_steps: 1 + num_sanity_val_steps: 1 + val_check_interval: 100 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/prolificdreamer-geometry.yaml b/configs/prolificdreamer-geometry.yaml new file mode 100644 index 0000000..928dcf3 --- /dev/null +++ b/configs/prolificdreamer-geometry.yaml @@ -0,0 +1,84 @@ +name: "prolificdreamer-geometry" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: 1 + width: 512 + height: 512 + camera_distance_range: [1.0, 1.5] + fovy_range: [40, 70] + elevation_range: [-10, 45] + camera_perturb: 0. + center_perturb: 0. + up_perturb: 0. + eval_camera_distance: 1.5 + eval_fovy_deg: 70. + +system_type: "prolificdreamer-system" +system: + stage: geometry + geometry_convert_from: ??? + geometry_type: "tetrahedra-sdf-grid" + geometry: + radius: 1.0 # consistent with coarse + isosurface_resolution: 128 + isosurface_deformable_grid: true + geometry_only: true + + material_type: "no-material" # unused + material: + n_output_dims: 0 + + background_type: "solid-color-background" # unused + + renderer_type: "nvdiff-rasterizer" + renderer: + context_type: gl + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 100. + min_step_percent: 0.02 + max_step_percent: [5000, 0.98, 0.5, 5001] # annealed to 0.5 after 5000 steps + weighting_strategy: sds + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 1. + lambda_normal_consistency: 10000. + lambda_laplacian_smoothness: 10000. + lambda_z_variance: 0. + + optimizer: + name: Adam + args: + lr: 0.005 + betas: [0.9, 0.99] + eps: 1.e-15 + +trainer: + max_steps: 15000 + log_every_n_steps: 1 + num_sanity_val_steps: 1 + val_check_interval: 200 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/prolificdreamer-hifa.yaml b/configs/prolificdreamer-hifa.yaml new file mode 100644 index 0000000..6f189b5 --- /dev/null +++ b/configs/prolificdreamer-hifa.yaml @@ -0,0 +1,118 @@ +name: "prolificdreamer-hifa" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: [1, 1] + # 0-4999: 64x64, >=5000: 512x512 + # this drastically reduces VRAM usage as empty space is pruned in early training + width: [64, 512] + height: [64, 512] + resolution_milestones: [5000] + camera_distance_range: [1.0, 1.5] + fovy_range: [40, 70] + elevation_range: [-10, 45] + camera_perturb: 0. + center_perturb: 0. + up_perturb: 0. + eval_camera_distance: 1.5 + eval_fovy_deg: 70. + +system_type: "prolificdreamer-system" +system: + stage: coarse + geometry_type: "implicit-volume" + geometry: + radius: 1.0 + normal_type: null + + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + + material_type: "no-material" + material: + n_output_dims: 3 + color_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + random_aug: true + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + front_threshold: 30. + back_threshold: 30. + + guidance_type: "stable-diffusion-vsd-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + pretrained_model_name_or_path_lora: "stabilityai/stable-diffusion-2-1" + guidance_scale: 7.5 + min_step_percent: 0.3 + max_step_percent: 0.98 # annealed to 0.5 after 5000 steps + sqrt_anneal: true # sqrt anneal proposed in HiFA: https://hifa-team.github.io/HiFA-site/ + use_img_loss: true # image-space SDS proposed in HiFA: https://hifa-team.github.io/HiFA-site/ + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_vsd: 1. + lambda_vsd_img: 0.01 + lambda_lora: 1. + lambda_orient: 0. + lambda_sparsity: 10. + lambda_opaque: [10000, 0.0, 1000.0, 10001] + lambda_z_variance: 300. + optimizer: + name: AdamW + args: + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry.encoding: + lr: 0.01 + geometry.density_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + background: + lr: 0.001 + guidance: + lr: 0.0001 + +trainer: + max_steps: 25000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/prolificdreamer-patch.yaml b/configs/prolificdreamer-patch.yaml new file mode 100644 index 0000000..1a2f026 --- /dev/null +++ b/configs/prolificdreamer-patch.yaml @@ -0,0 +1,114 @@ +name: "prolificdreamer-patch" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: 1 + width: 512 + height: 512 + camera_distance_range: [1.0, 1.5] + fovy_range: [40, 70] + elevation_range: [-10, 45] + camera_perturb: 0. + center_perturb: 0. + up_perturb: 0. + eval_camera_distance: 1.5 + eval_fovy_deg: 70. + +system_type: "prolificdreamer-system" +system: + stage: coarse + geometry_type: "implicit-volume" + geometry: + radius: 1.0 + normal_type: null + + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + + material_type: "no-material" + material: + n_output_dims: 3 + color_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + random_aug: true + + renderer_type: "patch-renderer" + renderer: + base_renderer_type: "nerf-volume-renderer" + base_renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + patch_size: 128 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + front_threshold: 30. + back_threshold: 30. + + guidance_type: "stable-diffusion-vsd-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + pretrained_model_name_or_path_lora: "stabilityai/stable-diffusion-2-1" + guidance_scale: 7.5 + min_step_percent: 0.02 + max_step_percent: [5000, 0.98, 0.5, 5001] # annealed to 0.5 after 5000 steps + + loggers: + wandb: + enable: false + project: "threestudio" + + loss: + lambda_vsd: 1. + lambda_lora: 1. + lambda_orient: 0. + lambda_sparsity: 10. + lambda_opaque: [10000, 0.0, 1000.0, 10001] + lambda_z_variance: 0. + optimizer: + name: AdamW + args: + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry.encoding: + lr: 0.01 + geometry.density_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + background: + lr: 0.001 + guidance: + lr: 0.0001 + +trainer: + max_steps: 25000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/prolificdreamer-scene-hifa.yaml b/configs/prolificdreamer-scene-hifa.yaml new file mode 100644 index 0000000..d9d3424 --- /dev/null +++ b/configs/prolificdreamer-scene-hifa.yaml @@ -0,0 +1,116 @@ +name: "prolificdreamer-hifa" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: [1, 1] + # 0-4999: 64x64, >=5000: 512x512 + # this drastically reduces VRAM usage as empty space is pruned in early training + width: [64, 512] + height: [64, 512] + resolution_milestones: [5000] + camera_distance_range: [0.1, 2.3] + fovy_range: [40, 70] + elevation_range: [-10, 45] + camera_perturb: 0. + center_perturb: 0. + up_perturb: 0. + eval_camera_distance: 2.0 + eval_fovy_deg: 70. + +system_type: "prolificdreamer-system" +system: + stage: coarse + geometry_type: "implicit-volume" + geometry: + radius: 5.0 + normal_type: null + + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: -10. + density_blob_std: 2.5 + + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + + material_type: "no-material" + material: + n_output_dims: 3 + color_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-vsd-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + pretrained_model_name_or_path_lora: "stabilityai/stable-diffusion-2-1" + guidance_scale: 7.5 + min_step_percent: 0.3 + max_step_percent: [10000, 0.98, 0.5, 10001] # annealed to 0.5 after 10000 steps + view_dependent_prompting: false + sqrt_anneal: true # sqrt anneal proposed in HiFA: https://hifa-team.github.io/HiFA-site/ + use_img_loss: true # image-space SDS proposed in HiFA: https://hifa-team.github.io/HiFA-site/ + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_vsd: 1. + lambda_vsd_img: 0.01 + lambda_lora: 1. + lambda_orient: 0. + lambda_sparsity: 0. + lambda_opaque: 0. + lambda_z_variance: 300. + optimizer: + name: AdamW + args: + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry.encoding: + lr: 0.01 + geometry.density_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + background: + lr: 0.001 + guidance: + lr: 0.0001 + +trainer: + max_steps: 25000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/prolificdreamer-scene.yaml b/configs/prolificdreamer-scene.yaml new file mode 100644 index 0000000..816718d --- /dev/null +++ b/configs/prolificdreamer-scene.yaml @@ -0,0 +1,114 @@ +name: "prolificdreamer" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: [1, 1] + # 0-4999: 64x64, >=5000: 512x512 + # this drastically reduces VRAM usage as empty space is pruned in early training + width: [64, 512] + height: [64, 512] + resolution_milestones: [5000] + camera_distance_range: [0.1, 2.3] + fovy_range: [40, 70] + elevation_range: [-10, 45] + camera_perturb: 0. + center_perturb: 0. + up_perturb: 0. + eval_camera_distance: 2.0 + eval_fovy_deg: 70. + +system_type: "prolificdreamer-system" +system: + stage: coarse + geometry_type: "implicit-volume" + geometry: + radius: 5.0 + normal_type: null + + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: -10. + density_blob_std: 2.5 + + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + + material_type: "no-material" + material: + n_output_dims: 3 + color_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-vsd-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + pretrained_model_name_or_path_lora: "stabilityai/stable-diffusion-2-1" + guidance_scale: 7.5 + min_step_percent: 0.02 + max_step_percent: [10000, 0.98, 0.5, 10001] # annealed to 0.5 after 10000 steps + view_dependent_prompting: false + sqrt_anneal: false + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_vsd: 1. + lambda_lora: 1. + lambda_orient: 0. + lambda_sparsity: 0. + lambda_opaque: 0. + lambda_z_variance: 1. + optimizer: + name: AdamW + args: + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry.encoding: + lr: 0.01 + geometry.density_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + background: + lr: 0.001 + guidance: + lr: 0.0001 + +trainer: + max_steps: 25000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/prolificdreamer-texture.yaml b/configs/prolificdreamer-texture.yaml new file mode 100644 index 0000000..a6f77d5 --- /dev/null +++ b/configs/prolificdreamer-texture.yaml @@ -0,0 +1,102 @@ +name: "prolificdreamer-texture" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: 1 + width: 512 + height: 512 + camera_distance_range: [1.0, 1.5] + fovy_range: [40, 70] + elevation_range: [-10, 45] + camera_perturb: 0. + center_perturb: 0. + up_perturb: 0. + eval_camera_distance: 1.5 + eval_fovy_deg: 70. + +system_type: "prolificdreamer-system" +system: + stage: texture + geometry_convert_from: ??? + geometry_type: "tetrahedra-sdf-grid" + geometry: + radius: 1.0 # consistent with last stage + isosurface_resolution: 128 # consistent with last stage + isosurface_deformable_grid: true + isosurface_remove_outliers: true + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + fix_geometry: true + + material_type: "no-material" + material: + n_output_dims: 3 + color_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + + renderer_type: "nvdiff-rasterizer" + renderer: + context_type: gl + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + front_threshold: 30. + back_threshold: 30. + + guidance_type: "stable-diffusion-vsd-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + pretrained_model_name_or_path_lora: "stabilityai/stable-diffusion-2-1" + guidance_scale: 7.5 + min_step_percent: 0.02 + max_step_percent: [5000, 0.98, 0.5, 5001] # annealed to 0.5 after 5000 steps + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_vsd: 1. + lambda_lora: 1. + optimizer: + name: AdamW + args: + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry.encoding: + lr: 0.01 + geometry.feature_network: + lr: 0.001 + background: + lr: 0.001 + guidance: + lr: 0.0001 + +trainer: + max_steps: 30000 + log_every_n_steps: 1 + num_sanity_val_steps: 1 + val_check_interval: 200 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/prolificdreamer.yaml b/configs/prolificdreamer.yaml new file mode 100644 index 0000000..43ae3d5 --- /dev/null +++ b/configs/prolificdreamer.yaml @@ -0,0 +1,116 @@ +name: "prolificdreamer" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: [1, 1] + # 0-4999: 64x64, >=5000: 512x512 + # this drastically reduces VRAM usage as empty space is pruned in early training + width: [64, 512] + height: [64, 512] + resolution_milestones: [5000] + camera_distance_range: [1.0, 1.5] + fovy_range: [40, 70] + elevation_range: [-10, 45] + camera_perturb: 0. + center_perturb: 0. + up_perturb: 0. + eval_camera_distance: 1.5 + eval_fovy_deg: 70. + +system_type: "prolificdreamer-system" +system: + stage: coarse + geometry_type: "implicit-volume" + geometry: + radius: 1.0 + normal_type: null + + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + + material_type: "no-material" + material: + n_output_dims: 3 + color_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + random_aug: true + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + front_threshold: 30. + back_threshold: 30. + + guidance_type: "stable-diffusion-vsd-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + pretrained_model_name_or_path_lora: "stabilityai/stable-diffusion-2-1" + guidance_scale: 7.5 + min_step_percent: 0.02 + max_step_percent: [5000, 0.98, 0.5, 5001] # annealed to 0.5 after 5000 steps + sqrt_anneal: false + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_vsd: 1. + lambda_lora: 1. + lambda_orient: 0. + lambda_sparsity: 10. + lambda_opaque: [10000, 0.0, 1000.0, 10001] + lambda_z_variance: 0. + optimizer: + name: AdamW + args: + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry.encoding: + lr: 0.01 + geometry.density_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + background: + lr: 0.001 + guidance: + lr: 0.0001 + +trainer: + max_steps: 25000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/sjc.yaml b/configs/sjc.yaml new file mode 100644 index 0000000..494493d --- /dev/null +++ b/configs/sjc.yaml @@ -0,0 +1,91 @@ +name: sjc +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: outputs +seed: 0 + +data_type: random-camera-datamodule +data: + camera_distance_range: [1.50, 1.50] + elevation_range: [-10, 45] + camera_perturb: 0.0 + center_perturb: 0.0 + up_perturb: 0.0 + light_position_perturb: 0.0 + eval_elevation_deg: 20.0 + +system_type: sjc-system +system: + geometry_type: volume-grid + geometry: + normal_type: null + grid_size: [100, 100, 100] + density_bias: -1.0 + n_feature_dims: 4 + + material_type: no-material + material: + n_output_dims: 4 + color_activation: none + + background_type: textured-background + background: + n_output_dims: 4 + color_activation: none + height: 4 + width: 4 + + renderer_type: nerf-volume-renderer + renderer: + num_samples_per_ray: 512 + grid_prune: false + + prompt_processor_type: stable-diffusion-prompt-processor + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + view_dependent_prompt_front: true + + guidance_type: stable-diffusion-guidance + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 100. + use_sjc: true + var_red: true + min_step_percent: 0.01 + max_step_percent: 0.97 + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 1. + center_ratio: 0.78125 # = 50 / 64 + lambda_depth: 0 # or try 10 + lambda_emptiness: [5000, 1.e+4, 2.e+5, 5001] + emptiness_scale: 10 + + optimizer: + name: Adamax + args: + lr: 0.05 + params: + geometry: + lr: 0.05 + background: + lr: 0.0001 # maybe 0.001/0.01 is better + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation tim + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/sketchshape-refine.yaml b/configs/sketchshape-refine.yaml new file mode 100644 index 0000000..d4728ce --- /dev/null +++ b/configs/sketchshape-refine.yaml @@ -0,0 +1,87 @@ +name: "sketchshape-refine" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + elevation_range: [-10, 45] + +system_type: "latentnerf-system" +system: + refinement: true + weights: ??? + weights_ignore_modules: ["material", "background"] + guide_shape: ??? + + geometry_type: "implicit-volume" + geometry: + n_feature_dims: 4 + normal_type: null + + material_type: "sd-latent-adapter-material" + + background_type: "neural-environment-map-background" + + renderer_type: "nerf-volume-renderer" + renderer: + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 100. + weighting_strategy: sds + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 1. + lambda_sparsity: 0.0 + lambda_shape: 1. + lambda_opaque: 0.0 + lambda_orient: 0.0 + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + scheduler: + name: SequentialLR + interval: step + warmup_steps: 100 + milestones: + - ${system.scheduler.warmup_steps} + schedulers: + - name: LinearLR # linear warm-up in the first system.warmup_steps steps + args: + start_factor: 0.1 + end_factor: 1.0 + total_iters: ${system.scheduler.warmup_steps} + - name: ExponentialLR + args: + gamma: ${calc_exp_lr_decay_rate:0.1,${sub:${trainer.max_steps},${system.scheduler.warmup_steps}}} + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 1 + val_check_interval: 200 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/sketchshape.yaml b/configs/sketchshape.yaml new file mode 100644 index 0000000..9acbd6c --- /dev/null +++ b/configs/sketchshape.yaml @@ -0,0 +1,90 @@ +name: "sketchshape" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + elevation_range: [-10, 45] + +system_type: "latentnerf-system" +system: + guide_shape: ??? + + geometry_type: "implicit-volume" + geometry: + n_feature_dims: 4 + normal_type: null + + material_type: "no-material" + material: + n_output_dims: 4 + color_activation: none + + background_type: "neural-environment-map-background" + background: + n_output_dims: 4 + color_activation: none + + renderer_type: "nerf-volume-renderer" + renderer: + num_samples_per_ray: 512 + + prompt_processor_type: "stable-diffusion-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + prompt: ??? + + guidance_type: "stable-diffusion-guidance" + guidance: + pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base" + guidance_scale: 100. + weighting_strategy: sds + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 1.0 + lambda_sparsity: 0.0 + lambda_shape: 1.0 + lambda_opaque: 0.0 + lambda_orient: 0.0 + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-15 + scheduler: + name: SequentialLR + interval: step + warmup_steps: 100 + milestones: + - ${system.scheduler.warmup_steps} + schedulers: + - name: LinearLR # linear warm-up in the first system.warmup_steps steps + args: + start_factor: 0.1 + end_factor: 1.0 + total_iters: ${system.scheduler.warmup_steps} + - name: ExponentialLR + args: + gamma: ${calc_exp_lr_decay_rate:0.1,${sub:${trainer.max_steps},${system.scheduler.warmup_steps}}} + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/stable-zero123.yaml b/configs/stable-zero123.yaml new file mode 100644 index 0000000..3f27555 --- /dev/null +++ b/configs/stable-zero123.yaml @@ -0,0 +1,147 @@ +name: "zero123-sai" +tag: "${data.random_camera.height}_${rmspace:${basename:${data.image_path}},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "single-image-datamodule" +data: # threestudio/data/image.py -> SingleImageDataModuleConfig + image_path: ./load/images/hamburger_rgba.png + height: [128, 256, 512] + width: [128, 256, 512] + resolution_milestones: [200, 300] + default_elevation_deg: 5.0 + default_azimuth_deg: 0.0 + default_camera_distance: 3.8 + default_fovy_deg: 20.0 + requires_depth: ${cmaxgt0orcmaxgt0:${system.loss.lambda_depth},${system.loss.lambda_depth_rel}} + requires_normal: ${cmaxgt0:${system.loss.lambda_normal}} + random_camera: # threestudio/data/uncond.py -> RandomCameraDataModuleConfig + height: [64, 128, 256] + width: [64, 128, 256] + batch_size: [8, 4, 2] + resolution_milestones: [200, 300] + eval_height: 512 + eval_width: 512 + eval_batch_size: 1 + elevation_range: [-10, 80] + azimuth_range: [-180, 180] + camera_distance_range: [3.8, 3.8] + fovy_range: [20.0, 20.0] # Zero123 has fixed fovy + progressive_until: 0 + camera_perturb: 0.0 + center_perturb: 0.0 + up_perturb: 0.0 + light_position_perturb: 1.0 + light_distance_range: [7.5, 10.0] + eval_elevation_deg: ${data.default_elevation_deg} + eval_camera_distance: ${data.default_camera_distance} + eval_fovy_deg: ${data.default_fovy_deg} + light_sample_strategy: "dreamfusion" + batch_uniform_azimuth: False + n_val_views: 30 + n_test_views: 120 + +system_type: "zero123-system" +system: + geometry_type: "implicit-volume" + geometry: + radius: 2.0 + normal_type: "analytic" + + # use Magic3D density initialization instead + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + # coarse to fine hash grid encoding + # to ensure smooth analytic normals + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + mlp_network_config: + otype: "VanillaMLP" + activation: "ReLU" + output_activation: "none" + n_neurons: 64 + n_hidden_layers: 2 + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 100000 + textureless_prob: 0.05 + albedo_activation: sigmoid + + background_type: "solid-color-background" # unused + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 256 + return_comp_normal: ${cmaxgt0:${system.loss.lambda_normal_smooth}} + return_normal_perturb: ${cmaxgt0:${system.loss.lambda_3d_normal_smooth}} + + prompt_processor_type: "dummy-prompt-processor" # Zero123 doesn't use prompts + prompt_processor: + pretrained_model_name_or_path: "" + prompt: "" + + guidance_type: "stable-zero123-guidance" + guidance: + pretrained_config: "./load/zero123/sd-objaverse-finetune-c_concat-256.yaml" + pretrained_model_name_or_path: "./load/zero123/stable_zero123.ckpt" + vram_O: ${not:${gt0:${system.freq.guidance_eval}}} + cond_image_path: ${data.image_path} + cond_elevation_deg: ${data.default_elevation_deg} + cond_azimuth_deg: ${data.default_azimuth_deg} + cond_camera_distance: ${data.default_camera_distance} + guidance_scale: 3.0 + min_step_percent: [50, 0.7, 0.3, 200] # (start_iter, start_val, end_val, end_iter) + max_step_percent: [50, 0.98, 0.8, 200] + + freq: + ref_only_steps: 0 + guidance_eval: 0 + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 0.1 + lambda_rgb: [100, 500., 1000., 400] + lambda_mask: 50. + lambda_depth: 0. # 0.05 + lambda_depth_rel: 0. # [0, 0, 0.05, 100] + lambda_normal: 0. # [0, 0, 0.05, 100] + lambda_normal_smooth: [100, 7.0, 5.0, 150, 10.0, 200] + lambda_3d_normal_smooth: [100, 7.0, 5.0, 150, 10.0, 200] + lambda_orient: 1.0 + lambda_sparsity: 0.5 # should be tweaked for every model + lambda_opaque: 0.5 + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-8 + +trainer: + max_steps: 600 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 100 + enable_progress_bar: true + precision: 32 + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: 100 # ${trainer.max_steps} diff --git a/configs/textmesh-if.yaml b/configs/textmesh-if.yaml new file mode 100644 index 0000000..8aadff8 --- /dev/null +++ b/configs/textmesh-if.yaml @@ -0,0 +1,105 @@ +name: "textmesh-if" +tag: "${rmspace:${system.prompt_processor.prompt},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "random-camera-datamodule" +data: + batch_size: 1 + width: 64 + height: 64 + camera_distance_range: [1.5, 2.0] + fovy_range: [40, 70] + elevation_range: [-10, 90] + light_sample_strategy: "dreamfusion" + eval_camera_distance: 2.0 + eval_fovy_deg: 70. + +system_type: "textmesh-system" +system: + geometry_type: "implicit-sdf" + geometry: + radius: 2.0 + normal_type: finite_difference + # progressive eps from Neuralangelo + finite_difference_normal_eps: progressive + + sdf_bias: sphere + sdf_bias_params: 0.5 + + # coarse to fine hash grid encoding + pos_encoding_config: + otype: ProgressiveBandHashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.381912879967776 # max resolution 2048 + start_level: 8 # resolution ~200 + start_step: 2000 + update_steps: 500 + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 2001 + albedo_activation: sigmoid + + background_type: "neural-environment-map-background" + background: + color_activation: sigmoid + + renderer_type: "neus-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + cos_anneal_end_steps: ${trainer.max_steps} + eval_chunk_size: 8192 + + prompt_processor_type: "deep-floyd-prompt-processor" + prompt_processor: + pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + prompt: ??? + + guidance_type: "deep-floyd-guidance" + guidance: + pretrained_model_name_or_path: "DeepFloyd/IF-I-XL-v1.0" + guidance_scale: 20. + weighting_strategy: sds + min_step_percent: 0.02 + max_step_percent: 0.98 + + loss: + lambda_sds: 1. + lambda_orient: 0.0 + lambda_sparsity: 0.0 + lambda_opaque: 0.0 + lambda_eikonal: 1000. + optimizer: + name: Adam + args: + betas: [0.9, 0.99] + eps: 1.e-15 + params: + geometry.encoding: + lr: 0.01 + geometry.sdf_network: + lr: 0.001 + geometry.feature_network: + lr: 0.001 + background: + lr: 0.001 + renderer: + lr: 0.001 + +trainer: + max_steps: 10000 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 200 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/zero123-geometry.yaml b/configs/zero123-geometry.yaml new file mode 100644 index 0000000..3590976 --- /dev/null +++ b/configs/zero123-geometry.yaml @@ -0,0 +1,153 @@ +name: "zero123_geom" +tag: "${data.random_camera.height}_${rmspace:${basename:${data.image_path}},_}_prog${data.random_camera.progressive_until}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "single-image-datamodule" +data: # threestudio/data/image.py -> SingleImageDataModuleConfig + image_path: ./load/images/hamburger_rgba.png + height: 512 + width: 512 + default_elevation_deg: 0.0 + default_azimuth_deg: 0.0 + default_camera_distance: 3.8 + default_fovy_deg: 20.0 + requires_depth: ${cmaxgt0orcmaxgt0:${system.loss.lambda_depth},${system.loss.lambda_depth_rel}} + requires_normal: ${cmaxgt0:${system.loss.lambda_normal}} + random_camera: # threestudio/data/uncond.py -> RandomCameraDataModuleConfig + height: 256 # Zero123 operates at 256x256 + width: 256 + batch_size: 8 + resolution_milestones: [] + eval_height: 512 + eval_width: 512 + eval_batch_size: 1 + elevation_range: [-10, 45] + azimuth_range: [-180, 180] + camera_distance_range: [3.8, 3.8] + fovy_range: [20.0, 20.0] # Zero123 has fixed fovy + progressive_until: 0 + camera_perturb: 0.0 + center_perturb: 0.0 + up_perturb: 0.0 + light_position_perturb: 1.0 + light_distance_range: [7.5, 10.0] + eval_elevation_deg: ${data.default_elevation_deg} + eval_camera_distance: ${data.default_camera_distance} + eval_fovy_deg: ${data.default_fovy_deg} + light_sample_strategy: "dreamfusion" + batch_uniform_azimuth: False + n_val_views: 30 + n_test_views: 120 + +system_type: "zero123-system" +system: + refinement: true + geometry_convert_from: ??? + geometry_convert_inherit_texture: true + geometry_type: "tetrahedra-sdf-grid" + geometry: + radius: 2.0 # consistent with coarse + isosurface_resolution: 128 + isosurface_deformable_grid: true + pos_encoding_config: # consistent with coarse, no progressive band + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.4472692374403782 # max resolution 4096 + mlp_network_config: + otype: "VanillaMLP" + activation: "ReLU" + output_activation: "none" + n_neurons: 64 + n_hidden_layers: 2 + fix_geometry: false # optimize grid sdf and deformation + + # material_type: "no-material" # unused + # material: + # n_output_dims: 0 + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 10000 + textureless_prob: 0. + + background_type: "solid-color-background" # unused + + # renderer_type: "nerf-volume-renderer" + # renderer: + # radius: ${system.geometry.radius} + # num_samples_per_ray: 512 + # return_comp_normal: ${gt0:${system.loss.lambda_normal_smooth}} + # return_normal_perturb: ${gt0:${system.loss.lambda_3d_normal_smooth}} + + renderer_type: "nvdiff-rasterizer" + renderer: + context_type: cuda + + prompt_processor_type: "dummy-prompt-processor" # Zero123 doesn't use prompts + prompt_processor: + pretrained_model_name_or_path: "" + prompt: "" + + guidance_type: "zero123-guidance" + guidance: + pretrained_model_name_or_path: "./load/zero123/zero123-xl.ckpt" + pretrained_config: "./load/zero123/sd-objaverse-finetune-c_concat-256.yaml" + vram_O: ${not:${gt0:${system.freq.guidance_eval}}} + cond_image_path: ${data.image_path} + cond_elevation_deg: ${data.default_elevation_deg} + cond_azimuth_deg: ${data.default_azimuth_deg} + cond_camera_distance: ${data.default_camera_distance} + guidance_scale: 3.0 + min_step_percent: 0.02 + # min_step_percent: [0, 0.4, 0.02, 2000] # (start_iter, start_val, end_val, end_iter) + max_step_percent: 0.5 + # max_step_percent: [0, 0.85, 0.85, 2000] + + freq: + ref_only_steps: 0 + guidance_eval: 0 # if "alternate", this must not be a multiple of system.freq.n_ref + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 1.0 + lambda_rgb: 10000.0 + lambda_mask: 100.0 + lambda_depth: 0. + lambda_depth_rel: 0. # [0.0, 0.0, 1.0, 10000] + lambda_normal: 0. # [0, 0, 0.05, 100] + lambda_normal_smooth: 0. + lambda_3d_normal_smooth: 0. + lambda_normal_consistency: [50, 10000.0, 1000.0, 51] # 10000. + lambda_laplacian_smoothness: 0. + lambda_orient: 0. + lambda_sparsity: 0. + lambda_opaque: 0. + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-8 + +trainer: + max_steps: 200 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 50 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: ${trainer.max_steps} diff --git a/configs/zero123.yaml b/configs/zero123.yaml new file mode 100644 index 0000000..0f6ade9 --- /dev/null +++ b/configs/zero123.yaml @@ -0,0 +1,158 @@ +name: "zero123" +tag: "${data.random_camera.height}_${rmspace:${basename:${data.image_path}},_}" +exp_root_dir: "outputs" +seed: 0 + +data_type: "single-image-datamodule" +data: # threestudio/data/image.py -> SingleImageDataModuleConfig + image_path: ./load/images/hamburger_rgba.png + height: [128, 256, 512] + width: [128, 256, 512] + resolution_milestones: [200, 300] + default_elevation_deg: 5.0 + default_azimuth_deg: 0.0 + default_camera_distance: 3.8 + default_fovy_deg: 20.0 + requires_depth: ${cmaxgt0orcmaxgt0:${system.loss.lambda_depth},${system.loss.lambda_depth_rel}} + requires_normal: ${cmaxgt0:${system.loss.lambda_normal}} + random_camera: # threestudio/data/uncond.py -> RandomCameraDataModuleConfig + height: [64, 128, 256] + width: [64, 128, 256] + batch_size: [12, 4, 2] + resolution_milestones: [200, 300] + eval_height: 512 + eval_width: 512 + eval_batch_size: 1 + elevation_range: [-10, 80] + azimuth_range: [-180, 180] + camera_distance_range: [3.8, 3.8] + fovy_range: [20.0, 20.0] # Zero123 has fixed fovy + progressive_until: 0 + camera_perturb: 0.0 + center_perturb: 0.0 + up_perturb: 0.0 + light_position_perturb: 1.0 + light_distance_range: [7.5, 10.0] + eval_elevation_deg: ${data.default_elevation_deg} + eval_camera_distance: ${data.default_camera_distance} + eval_fovy_deg: ${data.default_fovy_deg} + light_sample_strategy: "dreamfusion" + batch_uniform_azimuth: False + n_val_views: 30 + n_test_views: 120 + +system_type: "zero123-system" +system: + geometry_type: "implicit-volume" + geometry: + radius: 2.0 + normal_type: "analytic" + + # the density initialization proposed in the DreamFusion paper + # does not work very well + # density_bias: "blob_dreamfusion" + # density_activation: exp + # density_blob_scale: 5. + # density_blob_std: 0.2 + + # use Magic3D density initialization instead + density_bias: "blob_magic3d" + density_activation: softplus + density_blob_scale: 10. + density_blob_std: 0.5 + + # coarse to fine hash grid encoding + # to ensure smooth analytic normals + pos_encoding_config: + otype: HashGrid + n_levels: 16 + n_features_per_level: 2 + log2_hashmap_size: 19 + base_resolution: 16 + per_level_scale: 1.447269237440378 # max resolution 4096 + mlp_network_config: + otype: "VanillaMLP" + activation: "ReLU" + output_activation: "none" + n_neurons: 64 + n_hidden_layers: 2 + + material_type: "diffuse-with-point-light-material" + material: + ambient_only_steps: 100000 + textureless_prob: 0.05 + albedo_activation: sigmoid + + # background_type: "neural-environment-map-background" + # background: + # color_activation: sigmoid + + background_type: "solid-color-background" # unused + + renderer_type: "nerf-volume-renderer" + renderer: + radius: ${system.geometry.radius} + num_samples_per_ray: 512 + return_comp_normal: ${gt0:${system.loss.lambda_normal_smooth}} + return_normal_perturb: ${gt0:${system.loss.lambda_3d_normal_smooth}} + + prompt_processor_type: "dummy-prompt-processor" # Zero123 doesn't use prompts + prompt_processor: + pretrained_model_name_or_path: "" + prompt: "" + + guidance_type: "zero123-guidance" + guidance: + pretrained_model_name_or_path: "./load/zero123/zero123-xl.ckpt" + pretrained_config: "./load/zero123/sd-objaverse-finetune-c_concat-256.yaml" + vram_O: ${not:${gt0:${system.freq.guidance_eval}}} + cond_image_path: ${data.image_path} + cond_elevation_deg: ${data.default_elevation_deg} + cond_azimuth_deg: ${data.default_azimuth_deg} + cond_camera_distance: ${data.default_camera_distance} + guidance_scale: 3.0 + min_step_percent: [0, 0.4, 0.2, 200] # (start_iter, start_val, end_val, end_iter) + max_step_percent: [0, 0.85, 0.5, 200] + + freq: + ref_only_steps: 0 + guidance_eval: 0 + + loggers: + wandb: + enable: false + project: "threestudio" + name: None + + loss: + lambda_sds: 0.1 + lambda_rgb: 500. + lambda_mask: 50. + lambda_depth: 0. # 0.05 + lambda_depth_rel: 0. # [0, 0, 0.05, 100] + lambda_normal: 0. # [0, 0, 0.05, 100] + lambda_normal_smooth: 8.0 + lambda_3d_normal_smooth: 8.0 + lambda_orient: 1.0 + lambda_sparsity: 0.1 # should be tweaked for every model + lambda_opaque: 0.1 + + optimizer: + name: Adam + args: + lr: 0.01 + betas: [0.9, 0.99] + eps: 1.e-8 + +trainer: + max_steps: 600 + log_every_n_steps: 1 + num_sanity_val_steps: 0 + val_check_interval: 100 + enable_progress_bar: true + precision: 16-mixed + +checkpoint: + save_last: true # save at each validation time + save_top_k: -1 + every_n_train_steps: 100 # ${trainer.max_steps} diff --git a/custom/put_custom_extensions_here b/custom/put_custom_extensions_here new file mode 100644 index 0000000..e69de29 diff --git a/custom/threestudio-dreammesh4d b/custom/threestudio-dreammesh4d new file mode 160000 index 0000000..ce2ac5a --- /dev/null +++ b/custom/threestudio-dreammesh4d @@ -0,0 +1 @@ +Subproject commit ce2ac5a0ac3dff358729f1e43120c98fc10cd3b9 diff --git a/docker/Dockerfile b/docker/Dockerfile new file mode 100644 index 0000000..8c090f9 --- /dev/null +++ b/docker/Dockerfile @@ -0,0 +1,60 @@ +# Reference: +# https://github.com/cvpaperchallenge/Ascender +# https://github.com/nerfstudio-project/nerfstudio + +FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 + +ARG USER_NAME=dreamer +ARG GROUP_NAME=dreamers +ARG UID=1000 +ARG GID=1000 + +# Set compute capability for nerfacc and tiny-cuda-nn +# See https://developer.nvidia.com/cuda-gpus and limit number to speed-up build +ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0 7.5 8.0 8.6 8.9 9.0+PTX" +ENV TCNN_CUDA_ARCHITECTURES=90;89;86;80;75;70;61;60 +# Speed-up build for RTX 30xx +# ENV TORCH_CUDA_ARCH_LIST="8.6" +# ENV TCNN_CUDA_ARCHITECTURES=86 +# Speed-up build for RTX 40xx +# ENV TORCH_CUDA_ARCH_LIST="8.9" +# ENV TCNN_CUDA_ARCHITECTURES=89 + +ENV CUDA_HOME=/usr/local/cuda +ENV PATH=${CUDA_HOME}/bin:/home/${USER_NAME}/.local/bin:${PATH} +ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH} +ENV LIBRARY_PATH=${CUDA_HOME}/lib64/stubs:${LIBRARY_PATH} + +# apt install by root user +RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ + build-essential \ + curl \ + git \ + libegl1-mesa-dev \ + libgl1-mesa-dev \ + libgles2-mesa-dev \ + libglib2.0-0 \ + libsm6 \ + libxext6 \ + libxrender1 \ + python-is-python3 \ + python3.10-dev \ + python3-pip \ + wget \ + && rm -rf /var/lib/apt/lists/* + +# Change user to non-root user +RUN groupadd -g ${GID} ${GROUP_NAME} \ + && useradd -ms /bin/sh -u ${UID} -g ${GID} ${USER_NAME} +USER ${USER_NAME} + +RUN pip install --upgrade pip setuptools==69.5.1 ninja +RUN pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --index-url https://download.pytorch.org/whl/cu118 +# Install nerfacc and tiny-cuda-nn before installing requirements.txt +# because these two installations are time consuming and error prone +RUN pip install git+https://github.com/KAIR-BAIR/nerfacc.git@v0.5.2 +RUN pip install git+https://github.com/NVlabs/tiny-cuda-nn.git#subdirectory=bindings/torch + +COPY requirements.txt /tmp +RUN cd /tmp && pip install -r requirements.txt +WORKDIR /home/${USER_NAME}/threestudio diff --git a/docker/compose.yaml b/docker/compose.yaml new file mode 100644 index 0000000..b15559a --- /dev/null +++ b/docker/compose.yaml @@ -0,0 +1,23 @@ +services: + threestudio: + build: + context: ../ + dockerfile: docker/Dockerfile + args: + # you can set environment variables, otherwise default values will be used + USER_NAME: ${HOST_USER_NAME:-dreamer} # export HOST_USER_NAME=$USER + GROUP_NAME: ${HOST_GROUP_NAME:-dreamers} + UID: ${HOST_UID:-1000} # export HOST_UID=$(id -u) + GID: ${HOST_GID:-1000} # export HOST_GID=$(id -g) + shm_size: '4gb' + environment: + NVIDIA_DISABLE_REQUIRE: 1 # avoid wrong `nvidia-container-cli: requirement error` + tty: true + volumes: + - ../:/home/${HOST_USER_NAME:-dreamer}/threestudio + deploy: + resources: + reservations: + devices: + - driver: nvidia + capabilities: [gpu] diff --git a/docs/installation.md b/docs/installation.md new file mode 100644 index 0000000..177221c --- /dev/null +++ b/docs/installation.md @@ -0,0 +1,59 @@ +# Installation + +## Prerequisite + +- NVIDIA GPU with at least 6GB VRAM. The more memory you have, the more methods and higher resolutions you can try. +- [NVIDIA Driver](https://www.nvidia.com/Download/index.aspx) whose version is higher than the [Minimum Required Driver Version](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html) of CUDA Toolkit you want to use. + +## Install CUDA Toolkit + +You can skip this step if you have installed sufficiently new version or you use Docker. + +Install [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive). + +- Example for Ubuntu 22.04: + - Run [command for CUDA 11.8 Ubuntu 22.04](https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=22.04&target_type=deb_local) +- Example for Ubuntu on WSL2: + - `sudo apt-key del 7fa2af80` + - Run [command for CUDA 11.8 WSL-Ubuntu](https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_local) + +## Git Clone + +```bash +git clone https://github.com/threestudio-project/threestudio.git +cd threestudio/ +``` + +## Install threestudio via Docker + +1. [Install Docker Engine](https://docs.docker.com/engine/install/). + This document assumes you [install Docker Engine on Ubuntu](https://docs.docker.com/engine/install/ubuntu/). +2. [Create `docker` group](https://docs.docker.com/engine/install/linux-postinstall/). + Otherwise, you need to type `sudo docker` instead of `docker`. +3. [Install NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#setting-up-nvidia-container-toolkit). +4. If you use WSL2, [enable systemd](https://learn.microsoft.com/en-us/windows/wsl/wsl-config#systemd-support). +5. Edit [Dockerfile](../docker/Dockerfile) for your GPU to speed-up build. + The default Dockerfile takes into account many types of GPUs. +6. Run Docker via `docker compose`. + +```bash +cd docker/ +docker compose build # build Docker image +docker compose up -d # create and start a container in background +docker compose exec threestudio bash # run bash in the container + +# Enjoy threestudio! + +exit # or Ctrl+D +docker compose stop # stop the container +docker compose start # start the container +docker compose down # stop and remove the container +``` + +Note: The current Dockerfile will cause errors when using the OpenGL-based rasterizer of nvdiffrast. +You can use the CUDA-based rasterizer by adding commands or editing configs. + +- `system.renderer.context_type=cuda` for training +- `system.exporter.context_type=cuda` for exporting meshes + +[This comment by the nvdiffrast author](https://github.com/NVlabs/nvdiffrast/issues/94#issuecomment-1288566038) could be a guide to resolve this limitation. diff --git a/extern/__init__.py b/extern/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/extern/ldm_zero123/extras.py b/extern/ldm_zero123/extras.py new file mode 100755 index 0000000..1646d46 --- /dev/null +++ b/extern/ldm_zero123/extras.py @@ -0,0 +1,78 @@ +import logging +from contextlib import contextmanager +from pathlib import Path + +import torch +from omegaconf import OmegaConf + +from extern.ldm_zero123.util import instantiate_from_config + + +@contextmanager +def all_logging_disabled(highest_level=logging.CRITICAL): + """ + A context manager that will prevent any logging messages + triggered during the body from being processed. + + :param highest_level: the maximum logging level in use. + This would only need to be changed if a custom level greater than CRITICAL + is defined. + + https://gist.github.com/simon-weber/7853144 + """ + # two kind-of hacks here: + # * can't get the highest logging level in effect => delegate to the user + # * can't get the current module-level override => use an undocumented + # (but non-private!) interface + + previous_level = logging.root.manager.disable + + logging.disable(highest_level) + + try: + yield + finally: + logging.disable(previous_level) + + +def load_training_dir(train_dir, device, epoch="last"): + """Load a checkpoint and config from training directory""" + train_dir = Path(train_dir) + ckpt = list(train_dir.rglob(f"*{epoch}.ckpt")) + assert len(ckpt) == 1, f"found {len(ckpt)} matching ckpt files" + config = list(train_dir.rglob(f"*-project.yaml")) + assert len(ckpt) > 0, f"didn't find any config in {train_dir}" + if len(config) > 1: + print(f"found {len(config)} matching config files") + config = sorted(config)[-1] + print(f"selecting {config}") + else: + config = config[0] + + config = OmegaConf.load(config) + return load_model_from_config(config, ckpt[0], device) + + +def load_model_from_config(config, ckpt, device="cpu", verbose=False): + """Loads a model from config and a ckpt + if config is a path will use omegaconf to load + """ + if isinstance(config, (str, Path)): + config = OmegaConf.load(config) + + with all_logging_disabled(): + print(f"Loading model from {ckpt}") + pl_sd = torch.load(ckpt, map_location="cpu") + global_step = pl_sd["global_step"] + sd = pl_sd["state_dict"] + model = instantiate_from_config(config.model) + m, u = model.load_state_dict(sd, strict=False) + if len(m) > 0 and verbose: + print("missing keys:") + print(m) + if len(u) > 0 and verbose: + print("unexpected keys:") + model.to(device) + model.eval() + model.cond_stage_model.device = device + return model diff --git a/extern/ldm_zero123/guidance.py b/extern/ldm_zero123/guidance.py new file mode 100755 index 0000000..a52a755 --- /dev/null +++ b/extern/ldm_zero123/guidance.py @@ -0,0 +1,110 @@ +import abc +from typing import List, Tuple + +import matplotlib.pyplot as plt +import numpy as np +import torch +from IPython.display import clear_output +from scipy import interpolate + + +class GuideModel(torch.nn.Module, abc.ABC): + def __init__(self) -> None: + super().__init__() + + @abc.abstractmethod + def preprocess(self, x_img): + pass + + @abc.abstractmethod + def compute_loss(self, inp): + pass + + +class Guider(torch.nn.Module): + def __init__(self, sampler, guide_model, scale=1.0, verbose=False): + """Apply classifier guidance + + Specify a guidance scale as either a scalar + Or a schedule as a list of tuples t = 0->1 and scale, e.g. + [(0, 10), (0.5, 20), (1, 50)] + """ + super().__init__() + self.sampler = sampler + self.index = 0 + self.show = verbose + self.guide_model = guide_model + self.history = [] + + if isinstance(scale, (Tuple, List)): + times = np.array([x[0] for x in scale]) + values = np.array([x[1] for x in scale]) + self.scale_schedule = {"times": times, "values": values} + else: + self.scale_schedule = float(scale) + + self.ddim_timesteps = sampler.ddim_timesteps + self.ddpm_num_timesteps = sampler.ddpm_num_timesteps + + def get_scales(self): + if isinstance(self.scale_schedule, float): + return len(self.ddim_timesteps) * [self.scale_schedule] + + interpolater = interpolate.interp1d( + self.scale_schedule["times"], self.scale_schedule["values"] + ) + fractional_steps = np.array(self.ddim_timesteps) / self.ddpm_num_timesteps + return interpolater(fractional_steps) + + def modify_score(self, model, e_t, x, t, c): + # TODO look up index by t + scale = self.get_scales()[self.index] + + if scale == 0: + return e_t + + sqrt_1ma = self.sampler.ddim_sqrt_one_minus_alphas[self.index].to(x.device) + with torch.enable_grad(): + x_in = x.detach().requires_grad_(True) + pred_x0 = model.predict_start_from_noise(x_in, t=t, noise=e_t) + x_img = model.first_stage_model.decode((1 / 0.18215) * pred_x0) + + inp = self.guide_model.preprocess(x_img) + loss = self.guide_model.compute_loss(inp) + grads = torch.autograd.grad(loss.sum(), x_in)[0] + correction = grads * scale + + if self.show: + clear_output(wait=True) + print( + loss.item(), + scale, + correction.abs().max().item(), + e_t.abs().max().item(), + ) + self.history.append( + [ + loss.item(), + scale, + correction.min().item(), + correction.max().item(), + ] + ) + plt.imshow( + (inp[0].detach().permute(1, 2, 0).clamp(-1, 1).cpu() + 1) / 2 + ) + plt.axis("off") + plt.show() + plt.imshow(correction[0][0].detach().cpu()) + plt.axis("off") + plt.show() + + e_t_mod = e_t - sqrt_1ma * correction + if self.show: + fig, axs = plt.subplots(1, 3) + axs[0].imshow(e_t[0][0].detach().cpu(), vmin=-2, vmax=+2) + axs[1].imshow(e_t_mod[0][0].detach().cpu(), vmin=-2, vmax=+2) + axs[2].imshow(correction[0][0].detach().cpu(), vmin=-2, vmax=+2) + plt.show() + self.index += 1 + return e_t_mod diff --git a/extern/ldm_zero123/lr_scheduler.py b/extern/ldm_zero123/lr_scheduler.py new file mode 100755 index 0000000..b2f4d38 --- /dev/null +++ b/extern/ldm_zero123/lr_scheduler.py @@ -0,0 +1,135 @@ +import numpy as np + + +class LambdaWarmUpCosineScheduler: + """ + note: use with a base_lr of 1.0 + """ + + def __init__( + self, + warm_up_steps, + lr_min, + lr_max, + lr_start, + max_decay_steps, + verbosity_interval=0, + ): + self.lr_warm_up_steps = warm_up_steps + self.lr_start = lr_start + self.lr_min = lr_min + self.lr_max = lr_max + self.lr_max_decay_steps = max_decay_steps + self.last_lr = 0.0 + self.verbosity_interval = verbosity_interval + + def schedule(self, n, **kwargs): + if self.verbosity_interval > 0: + if n % self.verbosity_interval == 0: + print(f"current step: {n}, recent lr-multiplier: {self.last_lr}") + if n < self.lr_warm_up_steps: + lr = ( + self.lr_max - self.lr_start + ) / self.lr_warm_up_steps * n + self.lr_start + self.last_lr = lr + return lr + else: + t = (n - self.lr_warm_up_steps) / ( + self.lr_max_decay_steps - self.lr_warm_up_steps + ) + t = min(t, 1.0) + lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * ( + 1 + np.cos(t * np.pi) + ) + self.last_lr = lr + return lr + + def __call__(self, n, **kwargs): + return self.schedule(n, **kwargs) + + +class LambdaWarmUpCosineScheduler2: + """ + supports repeated iterations, configurable via lists + note: use with a base_lr of 1.0. + """ + + def __init__( + self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0 + ): + assert ( + len(warm_up_steps) + == len(f_min) + == len(f_max) + == len(f_start) + == len(cycle_lengths) + ) + self.lr_warm_up_steps = warm_up_steps + self.f_start = f_start + self.f_min = f_min + self.f_max = f_max + self.cycle_lengths = cycle_lengths + self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths)) + self.last_f = 0.0 + self.verbosity_interval = verbosity_interval + + def find_in_interval(self, n): + interval = 0 + for cl in self.cum_cycles[1:]: + if n <= cl: + return interval + interval += 1 + + def schedule(self, n, **kwargs): + cycle = self.find_in_interval(n) + n = n - self.cum_cycles[cycle] + if self.verbosity_interval > 0: + if n % self.verbosity_interval == 0: + print( + f"current step: {n}, recent lr-multiplier: {self.last_f}, " + f"current cycle {cycle}" + ) + if n < self.lr_warm_up_steps[cycle]: + f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[ + cycle + ] * n + self.f_start[cycle] + self.last_f = f + return f + else: + t = (n - self.lr_warm_up_steps[cycle]) / ( + self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle] + ) + t = min(t, 1.0) + f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * ( + 1 + np.cos(t * np.pi) + ) + self.last_f = f + return f + + def __call__(self, n, **kwargs): + return self.schedule(n, **kwargs) + + +class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2): + def schedule(self, n, **kwargs): + cycle = self.find_in_interval(n) + n = n - self.cum_cycles[cycle] + if self.verbosity_interval > 0: + if n % self.verbosity_interval == 0: + print( + f"current step: {n}, recent lr-multiplier: {self.last_f}, " + f"current cycle {cycle}" + ) + + if n < self.lr_warm_up_steps[cycle]: + f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[ + cycle + ] * n + self.f_start[cycle] + self.last_f = f + return f + else: + f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * ( + self.cycle_lengths[cycle] - n + ) / (self.cycle_lengths[cycle]) + self.last_f = f + return f diff --git a/extern/ldm_zero123/models/autoencoder.py b/extern/ldm_zero123/models/autoencoder.py new file mode 100755 index 0000000..a6c16b3 --- /dev/null +++ b/extern/ldm_zero123/models/autoencoder.py @@ -0,0 +1,551 @@ +from contextlib import contextmanager + +import pytorch_lightning as pl +import torch +import torch.nn.functional as F +from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer + +from extern.ldm_zero123.modules.diffusionmodules.model import Decoder, Encoder +from extern.ldm_zero123.modules.distributions.distributions import ( + DiagonalGaussianDistribution, +) +from extern.ldm_zero123.util import instantiate_from_config + + +class VQModel(pl.LightningModule): + def __init__( + self, + ddconfig, + lossconfig, + n_embed, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + batch_resize_range=None, + scheduler_config=None, + lr_g_factor=1.0, + remap=None, + sane_index_shape=False, # tell vector quantizer to return indices as bhw + use_ema=False, + ): + super().__init__() + self.embed_dim = embed_dim + self.n_embed = n_embed + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.loss = instantiate_from_config(lossconfig) + self.quantize = VectorQuantizer( + n_embed, + embed_dim, + beta=0.25, + remap=remap, + sane_index_shape=sane_index_shape, + ) + self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + if colorize_nlabels is not None: + assert type(colorize_nlabels) == int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + self.batch_resize_range = batch_resize_range + if self.batch_resize_range is not None: + print( + f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}." + ) + + self.use_ema = use_ema + if self.use_ema: + self.model_ema = LitEma(self) + print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + self.scheduler_config = scheduler_config + self.lr_g_factor = lr_g_factor + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.parameters()) + self.model_ema.copy_to(self) + if context is not None: + print(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.parameters()) + if context is not None: + print(f"{context}: Restored training weights") + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu")["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = self.load_state_dict(sd, strict=False) + print( + f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys" + ) + if len(missing) > 0: + print(f"Missing Keys: {missing}") + print(f"Unexpected Keys: {unexpected}") + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self) + + def encode(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + quant, emb_loss, info = self.quantize(h) + return quant, emb_loss, info + + def encode_to_prequant(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + return h + + def decode(self, quant): + quant = self.post_quant_conv(quant) + dec = self.decoder(quant) + return dec + + def decode_code(self, code_b): + quant_b = self.quantize.embed_code(code_b) + dec = self.decode(quant_b) + return dec + + def forward(self, input, return_pred_indices=False): + quant, diff, (_, _, ind) = self.encode(input) + dec = self.decode(quant) + if return_pred_indices: + return dec, diff, ind + return dec, diff + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() + if self.batch_resize_range is not None: + lower_size = self.batch_resize_range[0] + upper_size = self.batch_resize_range[1] + if self.global_step <= 4: + # do the first few batches with max size to avoid later oom + new_resize = upper_size + else: + new_resize = np.random.choice( + np.arange(lower_size, upper_size + 16, 16) + ) + if new_resize != x.shape[2]: + x = F.interpolate(x, size=new_resize, mode="bicubic") + x = x.detach() + return x + + def training_step(self, batch, batch_idx, optimizer_idx): + # https://github.com/pytorch/pytorch/issues/37142 + # try not to fool the heuristics + x = self.get_input(batch, self.image_key) + xrec, qloss, ind = self(x, return_pred_indices=True) + + if optimizer_idx == 0: + # autoencode + aeloss, log_dict_ae = self.loss( + qloss, + x, + xrec, + optimizer_idx, + self.global_step, + last_layer=self.get_last_layer(), + split="train", + predicted_indices=ind, + ) + + self.log_dict( + log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True + ) + return aeloss + + if optimizer_idx == 1: + # discriminator + discloss, log_dict_disc = self.loss( + qloss, + x, + xrec, + optimizer_idx, + self.global_step, + last_layer=self.get_last_layer(), + split="train", + ) + self.log_dict( + log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True + ) + return discloss + + def validation_step(self, batch, batch_idx): + log_dict = self._validation_step(batch, batch_idx) + with self.ema_scope(): + log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema") + return log_dict + + def _validation_step(self, batch, batch_idx, suffix=""): + x = self.get_input(batch, self.image_key) + xrec, qloss, ind = self(x, return_pred_indices=True) + aeloss, log_dict_ae = self.loss( + qloss, + x, + xrec, + 0, + self.global_step, + last_layer=self.get_last_layer(), + split="val" + suffix, + predicted_indices=ind, + ) + + discloss, log_dict_disc = self.loss( + qloss, + x, + xrec, + 1, + self.global_step, + last_layer=self.get_last_layer(), + split="val" + suffix, + predicted_indices=ind, + ) + rec_loss = log_dict_ae[f"val{suffix}/rec_loss"] + self.log( + f"val{suffix}/rec_loss", + rec_loss, + prog_bar=True, + logger=True, + on_step=False, + on_epoch=True, + sync_dist=True, + ) + self.log( + f"val{suffix}/aeloss", + aeloss, + prog_bar=True, + logger=True, + on_step=False, + on_epoch=True, + sync_dist=True, + ) + if version.parse(pl.__version__) >= version.parse("1.4.0"): + del log_dict_ae[f"val{suffix}/rec_loss"] + self.log_dict(log_dict_ae) + self.log_dict(log_dict_disc) + return self.log_dict + + def configure_optimizers(self): + lr_d = self.learning_rate + lr_g = self.lr_g_factor * self.learning_rate + print("lr_d", lr_d) + print("lr_g", lr_g) + opt_ae = torch.optim.Adam( + list(self.encoder.parameters()) + + list(self.decoder.parameters()) + + list(self.quantize.parameters()) + + list(self.quant_conv.parameters()) + + list(self.post_quant_conv.parameters()), + lr=lr_g, + betas=(0.5, 0.9), + ) + opt_disc = torch.optim.Adam( + self.loss.discriminator.parameters(), lr=lr_d, betas=(0.5, 0.9) + ) + + if self.scheduler_config is not None: + scheduler = instantiate_from_config(self.scheduler_config) + + print("Setting up LambdaLR scheduler...") + scheduler = [ + { + "scheduler": LambdaLR(opt_ae, lr_lambda=scheduler.schedule), + "interval": "step", + "frequency": 1, + }, + { + "scheduler": LambdaLR(opt_disc, lr_lambda=scheduler.schedule), + "interval": "step", + "frequency": 1, + }, + ] + return [opt_ae, opt_disc], scheduler + return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + + def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if only_inputs: + log["inputs"] = x + return log + xrec, _ = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["inputs"] = x + log["reconstructions"] = xrec + if plot_ema: + with self.ema_scope(): + xrec_ema, _ = self(x) + if x.shape[1] > 3: + xrec_ema = self.to_rgb(xrec_ema) + log["reconstructions_ema"] = xrec_ema + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0 + return x + + +class VQModelInterface(VQModel): + def __init__(self, embed_dim, *args, **kwargs): + super().__init__(embed_dim=embed_dim, *args, **kwargs) + self.embed_dim = embed_dim + + def encode(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + return h + + def decode(self, h, force_not_quantize=False): + # also go through quantization layer + if not force_not_quantize: + quant, emb_loss, info = self.quantize(h) + else: + quant = h + quant = self.post_quant_conv(quant) + dec = self.decoder(quant) + return dec + + +class AutoencoderKL(pl.LightningModule): + def __init__( + self, + ddconfig, + lossconfig, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + ): + super().__init__() + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.loss = instantiate_from_config(lossconfig) + assert ddconfig["double_z"] + self.quant_conv = torch.nn.Conv2d(2 * ddconfig["z_channels"], 2 * embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + self.embed_dim = embed_dim + if colorize_nlabels is not None: + assert type(colorize_nlabels) == int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu")["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + self.load_state_dict(sd, strict=False) + print(f"Restored from {path}") + + def encode(self, x): + h = self.encoder(x) + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + return posterior + + def decode(self, z): + z = self.post_quant_conv(z) + dec = self.decoder(z) + return dec + + def forward(self, input, sample_posterior=True): + posterior = self.encode(input) + if sample_posterior: + z = posterior.sample() + else: + z = posterior.mode() + dec = self.decode(z) + return dec, posterior + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() + return x + + def training_step(self, batch, batch_idx, optimizer_idx): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + + if optimizer_idx == 0: + # train encoder+decoder+logvar + aeloss, log_dict_ae = self.loss( + inputs, + reconstructions, + posterior, + optimizer_idx, + self.global_step, + last_layer=self.get_last_layer(), + split="train", + ) + self.log( + "aeloss", + aeloss, + prog_bar=True, + logger=True, + on_step=True, + on_epoch=True, + ) + self.log_dict( + log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False + ) + return aeloss + + if optimizer_idx == 1: + # train the discriminator + discloss, log_dict_disc = self.loss( + inputs, + reconstructions, + posterior, + optimizer_idx, + self.global_step, + last_layer=self.get_last_layer(), + split="train", + ) + + self.log( + "discloss", + discloss, + prog_bar=True, + logger=True, + on_step=True, + on_epoch=True, + ) + self.log_dict( + log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False + ) + return discloss + + def validation_step(self, batch, batch_idx): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + aeloss, log_dict_ae = self.loss( + inputs, + reconstructions, + posterior, + 0, + self.global_step, + last_layer=self.get_last_layer(), + split="val", + ) + + discloss, log_dict_disc = self.loss( + inputs, + reconstructions, + posterior, + 1, + self.global_step, + last_layer=self.get_last_layer(), + split="val", + ) + + self.log("val/rec_loss", log_dict_ae["val/rec_loss"]) + self.log_dict(log_dict_ae) + self.log_dict(log_dict_disc) + return self.log_dict + + def configure_optimizers(self): + lr = self.learning_rate + opt_ae = torch.optim.Adam( + list(self.encoder.parameters()) + + list(self.decoder.parameters()) + + list(self.quant_conv.parameters()) + + list(self.post_quant_conv.parameters()), + lr=lr, + betas=(0.5, 0.9), + ) + opt_disc = torch.optim.Adam( + self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9) + ) + return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + + @torch.no_grad() + def log_images(self, batch, only_inputs=False, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if not only_inputs: + xrec, posterior = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["samples"] = self.decode(torch.randn_like(posterior.sample())) + log["reconstructions"] = xrec + log["inputs"] = x + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0 + return x + + +class IdentityFirstStage(torch.nn.Module): + def __init__(self, *args, vq_interface=False, **kwargs): + self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff + super().__init__() + + def encode(self, x, *args, **kwargs): + return x + + def decode(self, x, *args, **kwargs): + return x + + def quantize(self, x, *args, **kwargs): + if self.vq_interface: + return x, None, [None, None, None] + return x + + def forward(self, x, *args, **kwargs): + return x diff --git a/extern/ldm_zero123/models/diffusion/__init__.py b/extern/ldm_zero123/models/diffusion/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/extern/ldm_zero123/models/diffusion/classifier.py b/extern/ldm_zero123/models/diffusion/classifier.py new file mode 100755 index 0000000..40467e9 --- /dev/null +++ b/extern/ldm_zero123/models/diffusion/classifier.py @@ -0,0 +1,319 @@ +import os +from copy import deepcopy +from glob import glob + +import pytorch_lightning as pl +import torch +from einops import rearrange +from natsort import natsorted +from omegaconf import OmegaConf +from torch.nn import functional as F +from torch.optim import AdamW +from torch.optim.lr_scheduler import LambdaLR + +from extern.ldm_zero123.modules.diffusionmodules.openaimodel import ( + EncoderUNetModel, + UNetModel, +) +from extern.ldm_zero123.util import ( + default, + instantiate_from_config, + ismap, + log_txt_as_img, +) + +__models__ = {"class_label": EncoderUNetModel, "segmentation": UNetModel} + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +class NoisyLatentImageClassifier(pl.LightningModule): + def __init__( + self, + diffusion_path, + num_classes, + ckpt_path=None, + pool="attention", + label_key=None, + diffusion_ckpt_path=None, + scheduler_config=None, + weight_decay=1.0e-2, + log_steps=10, + monitor="val/loss", + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + self.num_classes = num_classes + # get latest config of diffusion model + diffusion_config = natsorted( + glob(os.path.join(diffusion_path, "configs", "*-project.yaml")) + )[-1] + self.diffusion_config = OmegaConf.load(diffusion_config).model + self.diffusion_config.params.ckpt_path = diffusion_ckpt_path + self.load_diffusion() + + self.monitor = monitor + self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1 + self.log_time_interval = self.diffusion_model.num_timesteps // log_steps + self.log_steps = log_steps + + self.label_key = ( + label_key + if not hasattr(self.diffusion_model, "cond_stage_key") + else self.diffusion_model.cond_stage_key + ) + + assert ( + self.label_key is not None + ), "label_key neither in diffusion model nor in model.params" + + if self.label_key not in __models__: + raise NotImplementedError() + + self.load_classifier(ckpt_path, pool) + + self.scheduler_config = scheduler_config + self.use_scheduler = self.scheduler_config is not None + self.weight_decay = weight_decay + + def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): + sd = torch.load(path, map_location="cpu") + if "state_dict" in list(sd.keys()): + sd = sd["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = ( + self.load_state_dict(sd, strict=False) + if not only_model + else self.model.load_state_dict(sd, strict=False) + ) + print( + f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys" + ) + if len(missing) > 0: + print(f"Missing Keys: {missing}") + if len(unexpected) > 0: + print(f"Unexpected Keys: {unexpected}") + + def load_diffusion(self): + model = instantiate_from_config(self.diffusion_config) + self.diffusion_model = model.eval() + self.diffusion_model.train = disabled_train + for param in self.diffusion_model.parameters(): + param.requires_grad = False + + def load_classifier(self, ckpt_path, pool): + model_config = deepcopy(self.diffusion_config.params.unet_config.params) + model_config.in_channels = ( + self.diffusion_config.params.unet_config.params.out_channels + ) + model_config.out_channels = self.num_classes + if self.label_key == "class_label": + model_config.pool = pool + + self.model = __models__[self.label_key](**model_config) + if ckpt_path is not None: + print( + "#####################################################################" + ) + print(f'load from ckpt "{ckpt_path}"') + print( + "#####################################################################" + ) + self.init_from_ckpt(ckpt_path) + + @torch.no_grad() + def get_x_noisy(self, x, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x)) + continuous_sqrt_alpha_cumprod = None + if self.diffusion_model.use_continuous_noise: + continuous_sqrt_alpha_cumprod = ( + self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1) + ) + # todo: make sure t+1 is correct here + + return self.diffusion_model.q_sample( + x_start=x, + t=t, + noise=noise, + continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod, + ) + + def forward(self, x_noisy, t, *args, **kwargs): + return self.model(x_noisy, t) + + @torch.no_grad() + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = rearrange(x, "b h w c -> b c h w") + x = x.to(memory_format=torch.contiguous_format).float() + return x + + @torch.no_grad() + def get_conditioning(self, batch, k=None): + if k is None: + k = self.label_key + assert k is not None, "Needs to provide label key" + + targets = batch[k].to(self.device) + + if self.label_key == "segmentation": + targets = rearrange(targets, "b h w c -> b c h w") + for down in range(self.numd): + h, w = targets.shape[-2:] + targets = F.interpolate(targets, size=(h // 2, w // 2), mode="nearest") + + # targets = rearrange(targets,'b c h w -> b h w c') + + return targets + + def compute_top_k(self, logits, labels, k, reduction="mean"): + _, top_ks = torch.topk(logits, k, dim=1) + if reduction == "mean": + return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item() + elif reduction == "none": + return (top_ks == labels[:, None]).float().sum(dim=-1) + + def on_train_epoch_start(self): + # save some memory + self.diffusion_model.model.to("cpu") + + @torch.no_grad() + def write_logs(self, loss, logits, targets): + log_prefix = "train" if self.training else "val" + log = {} + log[f"{log_prefix}/loss"] = loss.mean() + log[f"{log_prefix}/acc@1"] = self.compute_top_k( + logits, targets, k=1, reduction="mean" + ) + log[f"{log_prefix}/acc@5"] = self.compute_top_k( + logits, targets, k=5, reduction="mean" + ) + + self.log_dict( + log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True + ) + self.log("loss", log[f"{log_prefix}/loss"], prog_bar=True, logger=False) + self.log( + "global_step", self.global_step, logger=False, on_epoch=False, prog_bar=True + ) + lr = self.optimizers().param_groups[0]["lr"] + self.log("lr_abs", lr, on_step=True, logger=True, on_epoch=False, prog_bar=True) + + def shared_step(self, batch, t=None): + x, *_ = self.diffusion_model.get_input( + batch, k=self.diffusion_model.first_stage_key + ) + targets = self.get_conditioning(batch) + if targets.dim() == 4: + targets = targets.argmax(dim=1) + if t is None: + t = torch.randint( + 0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device + ).long() + else: + t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long() + x_noisy = self.get_x_noisy(x, t) + logits = self(x_noisy, t) + + loss = F.cross_entropy(logits, targets, reduction="none") + + self.write_logs(loss.detach(), logits.detach(), targets.detach()) + + loss = loss.mean() + return loss, logits, x_noisy, targets + + def training_step(self, batch, batch_idx): + loss, *_ = self.shared_step(batch) + return loss + + def reset_noise_accs(self): + self.noisy_acc = { + t: {"acc@1": [], "acc@5": []} + for t in range( + 0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t + ) + } + + def on_validation_start(self): + self.reset_noise_accs() + + @torch.no_grad() + def validation_step(self, batch, batch_idx): + loss, *_ = self.shared_step(batch) + + for t in self.noisy_acc: + _, logits, _, targets = self.shared_step(batch, t) + self.noisy_acc[t]["acc@1"].append( + self.compute_top_k(logits, targets, k=1, reduction="mean") + ) + self.noisy_acc[t]["acc@5"].append( + self.compute_top_k(logits, targets, k=5, reduction="mean") + ) + + return loss + + def configure_optimizers(self): + optimizer = AdamW( + self.model.parameters(), + lr=self.learning_rate, + weight_decay=self.weight_decay, + ) + + if self.use_scheduler: + scheduler = instantiate_from_config(self.scheduler_config) + + print("Setting up LambdaLR scheduler...") + scheduler = [ + { + "scheduler": LambdaLR(optimizer, lr_lambda=scheduler.schedule), + "interval": "step", + "frequency": 1, + } + ] + return [optimizer], scheduler + + return optimizer + + @torch.no_grad() + def log_images(self, batch, N=8, *args, **kwargs): + log = dict() + x = self.get_input(batch, self.diffusion_model.first_stage_key) + log["inputs"] = x + + y = self.get_conditioning(batch) + + if self.label_key == "class_label": + y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) + log["labels"] = y + + if ismap(y): + log["labels"] = self.diffusion_model.to_rgb(y) + + for step in range(self.log_steps): + current_time = step * self.log_time_interval + + _, logits, x_noisy, _ = self.shared_step(batch, t=current_time) + + log[f"inputs@t{current_time}"] = x_noisy + + pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes) + pred = rearrange(pred, "b h w c -> b c h w") + + log[f"pred@t{current_time}"] = self.diffusion_model.to_rgb(pred) + + for key in log: + log[key] = log[key][:N] + + return log diff --git a/extern/ldm_zero123/models/diffusion/ddim.py b/extern/ldm_zero123/models/diffusion/ddim.py new file mode 100755 index 0000000..d6b6e96 --- /dev/null +++ b/extern/ldm_zero123/models/diffusion/ddim.py @@ -0,0 +1,489 @@ +"""SAMPLING ONLY.""" + +from functools import partial + +import numpy as np +import torch +from einops import rearrange +from tqdm import tqdm + +from extern.ldm_zero123.models.diffusion.sampling_util import ( + norm_thresholding, + renorm_thresholding, + spatial_norm_thresholding, +) +from extern.ldm_zero123.modules.diffusionmodules.util import ( + extract_into_tensor, + make_ddim_sampling_parameters, + make_ddim_timesteps, + noise_like, +) + + +class DDIMSampler(object): + def __init__(self, model, schedule="linear", **kwargs): + super().__init__() + self.model = model + self.ddpm_num_timesteps = model.num_timesteps + self.schedule = schedule + + def to(self, device): + """Same as to in torch module + Don't really underestand why this isn't a module in the first place""" + for k, v in self.__dict__.items(): + if isinstance(v, torch.Tensor): + new_v = getattr(self, k).to(device) + setattr(self, k, new_v) + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != torch.device("cuda"): + attr = attr.to(torch.device("cuda")) + setattr(self, name, attr) + + def make_schedule( + self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True + ): + self.ddim_timesteps = make_ddim_timesteps( + ddim_discr_method=ddim_discretize, + num_ddim_timesteps=ddim_num_steps, + num_ddpm_timesteps=self.ddpm_num_timesteps, + verbose=verbose, + ) + alphas_cumprod = self.model.alphas_cumprod + assert ( + alphas_cumprod.shape[0] == self.ddpm_num_timesteps + ), "alphas have to be defined for each timestep" + to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) + + self.register_buffer("betas", to_torch(self.model.betas)) + self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod)) + self.register_buffer( + "alphas_cumprod_prev", to_torch(self.model.alphas_cumprod_prev) + ) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer( + "sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod.cpu())) + ) + self.register_buffer( + "sqrt_one_minus_alphas_cumprod", + to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())), + ) + self.register_buffer( + "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod.cpu())) + ) + self.register_buffer( + "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu())) + ) + self.register_buffer( + "sqrt_recipm1_alphas_cumprod", + to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)), + ) + + # ddim sampling parameters + ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters( + alphacums=alphas_cumprod.cpu(), + ddim_timesteps=self.ddim_timesteps, + eta=ddim_eta, + verbose=verbose, + ) + self.register_buffer("ddim_sigmas", ddim_sigmas) + self.register_buffer("ddim_alphas", ddim_alphas) + self.register_buffer("ddim_alphas_prev", ddim_alphas_prev) + self.register_buffer("ddim_sqrt_one_minus_alphas", np.sqrt(1.0 - ddim_alphas)) + sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( + (1 - self.alphas_cumprod_prev) + / (1 - self.alphas_cumprod) + * (1 - self.alphas_cumprod / self.alphas_cumprod_prev) + ) + self.register_buffer( + "ddim_sigmas_for_original_num_steps", sigmas_for_original_sampling_steps + ) + + @torch.no_grad() + def sample( + self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0.0, + mask=None, + x0=None, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + dynamic_threshold=None, + **kwargs, + ): + if conditioning is not None: + if isinstance(conditioning, dict): + ctmp = conditioning[list(conditioning.keys())[0]] + while isinstance(ctmp, list): + ctmp = ctmp[0] + cbs = ctmp.shape[0] + if cbs != batch_size: + print( + f"Warning: Got {cbs} conditionings but batch-size is {batch_size}" + ) + + else: + if conditioning.shape[0] != batch_size: + print( + f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}" + ) + + self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + # print(f'Data shape for DDIM sampling is {size}, eta {eta}') + + samples, intermediates = self.ddim_sampling( + conditioning, + size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, + x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + dynamic_threshold=dynamic_threshold, + ) + return samples, intermediates + + @torch.no_grad() + def ddim_sampling( + self, + cond, + shape, + x_T=None, + ddim_use_original_steps=False, + callback=None, + timesteps=None, + quantize_denoised=False, + mask=None, + x0=None, + img_callback=None, + log_every_t=100, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + dynamic_threshold=None, + t_start=-1, + ): + device = self.model.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + if timesteps is None: + timesteps = ( + self.ddpm_num_timesteps + if ddim_use_original_steps + else self.ddim_timesteps + ) + elif timesteps is not None and not ddim_use_original_steps: + subset_end = ( + int( + min(timesteps / self.ddim_timesteps.shape[0], 1) + * self.ddim_timesteps.shape[0] + ) + - 1 + ) + timesteps = self.ddim_timesteps[:subset_end] + + timesteps = timesteps[:t_start] + + intermediates = {"x_inter": [img], "pred_x0": [img]} + time_range = ( + reversed(range(0, timesteps)) + if ddim_use_original_steps + else np.flip(timesteps) + ) + total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] + # print(f"Running DDIM Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc="DDIM Sampler", total=total_steps) + + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((b,), step, device=device, dtype=torch.long) + + if mask is not None: + assert x0 is not None + img_orig = self.model.q_sample( + x0, ts + ) # TODO: deterministic forward pass? + img = img_orig * mask + (1.0 - mask) * img + + outs = self.p_sample_ddim( + img, + cond, + ts, + index=index, + use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, + temperature=temperature, + noise_dropout=noise_dropout, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + dynamic_threshold=dynamic_threshold, + ) + img, pred_x0 = outs + if callback: + img = callback(i, img, pred_x0) + if img_callback: + img_callback(pred_x0, i) + + if index % log_every_t == 0 or index == total_steps - 1: + intermediates["x_inter"].append(img) + intermediates["pred_x0"].append(pred_x0) + + return img, intermediates + + @torch.no_grad() + def p_sample_ddim( + self, + x, + c, + t, + index, + repeat_noise=False, + use_original_steps=False, + quantize_denoised=False, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + dynamic_threshold=None, + ): + b, *_, device = *x.shape, x.device + + if unconditional_conditioning is None or unconditional_guidance_scale == 1.0: + e_t = self.model.apply_model(x, t, c) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t] * 2) + if isinstance(c, dict): + assert isinstance(unconditional_conditioning, dict) + c_in = dict() + for k in c: + if isinstance(c[k], list): + c_in[k] = [ + torch.cat([unconditional_conditioning[k][i], c[k][i]]) + for i in range(len(c[k])) + ] + else: + c_in[k] = torch.cat([unconditional_conditioning[k], c[k]]) + else: + c_in = torch.cat([unconditional_conditioning, c]) + e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) + e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) + + if score_corrector is not None: + assert self.model.parameterization == "eps" + e_t = score_corrector.modify_score( + self.model, e_t, x, t, c, **corrector_kwargs + ) + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = ( + self.model.alphas_cumprod_prev + if use_original_steps + else self.ddim_alphas_prev + ) + sqrt_one_minus_alphas = ( + self.model.sqrt_one_minus_alphas_cumprod + if use_original_steps + else self.ddim_sqrt_one_minus_alphas + ) + sigmas = ( + self.model.ddim_sigmas_for_original_num_steps + if use_original_steps + else self.ddim_sigmas + ) + # select parameters corresponding to the currently considered timestep + a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) + a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) + sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) + sqrt_one_minus_at = torch.full( + (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device + ) + + # current prediction for x_0 + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + + print(t, sqrt_one_minus_at, a_t) + + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + + if dynamic_threshold is not None: + pred_x0 = norm_thresholding(pred_x0, dynamic_threshold) + + # direction pointing to x_t + dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.0: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev, pred_x0 + + @torch.no_grad() + def encode( + self, + x0, + c, + t_enc, + use_original_steps=False, + return_intermediates=None, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + ): + num_reference_steps = ( + self.ddpm_num_timesteps + if use_original_steps + else self.ddim_timesteps.shape[0] + ) + + assert t_enc <= num_reference_steps + num_steps = t_enc + + if use_original_steps: + alphas_next = self.alphas_cumprod[:num_steps] + alphas = self.alphas_cumprod_prev[:num_steps] + else: + alphas_next = self.ddim_alphas[:num_steps] + alphas = torch.tensor(self.ddim_alphas_prev[:num_steps]) + + x_next = x0 + intermediates = [] + inter_steps = [] + for i in tqdm(range(num_steps), desc="Encoding Image"): + t = torch.full( + (x0.shape[0],), i, device=self.model.device, dtype=torch.long + ) + if unconditional_guidance_scale == 1.0: + noise_pred = self.model.apply_model(x_next, t, c) + else: + assert unconditional_conditioning is not None + e_t_uncond, noise_pred = torch.chunk( + self.model.apply_model( + torch.cat((x_next, x_next)), + torch.cat((t, t)), + torch.cat((unconditional_conditioning, c)), + ), + 2, + ) + noise_pred = e_t_uncond + unconditional_guidance_scale * ( + noise_pred - e_t_uncond + ) + + xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next + weighted_noise_pred = ( + alphas_next[i].sqrt() + * ((1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) + * noise_pred + ) + x_next = xt_weighted + weighted_noise_pred + if ( + return_intermediates + and i % (num_steps // return_intermediates) == 0 + and i < num_steps - 1 + ): + intermediates.append(x_next) + inter_steps.append(i) + elif return_intermediates and i >= num_steps - 2: + intermediates.append(x_next) + inter_steps.append(i) + + out = {"x_encoded": x_next, "intermediate_steps": inter_steps} + if return_intermediates: + out.update({"intermediates": intermediates}) + return x_next, out + + @torch.no_grad() + def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): + # fast, but does not allow for exact reconstruction + # t serves as an index to gather the correct alphas + if use_original_steps: + sqrt_alphas_cumprod = self.sqrt_alphas_cumprod + sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod + else: + sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) + sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas + + if noise is None: + noise = torch.randn_like(x0) + return ( + extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise + ) + + @torch.no_grad() + def decode( + self, + x_latent, + cond, + t_start, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + use_original_steps=False, + ): + timesteps = ( + np.arange(self.ddpm_num_timesteps) + if use_original_steps + else self.ddim_timesteps + ) + timesteps = timesteps[:t_start] + + time_range = np.flip(timesteps) + total_steps = timesteps.shape[0] + # print(f"Running DDIM Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc="Decoding image", total=total_steps) + x_dec = x_latent + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full( + (x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long + ) + x_dec, _ = self.p_sample_ddim( + x_dec, + cond, + ts, + index=index, + use_original_steps=use_original_steps, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + ) + return x_dec diff --git a/extern/ldm_zero123/models/diffusion/ddpm.py b/extern/ldm_zero123/models/diffusion/ddpm.py new file mode 100755 index 0000000..590b692 --- /dev/null +++ b/extern/ldm_zero123/models/diffusion/ddpm.py @@ -0,0 +1,2689 @@ +""" +wild mixture of +https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py +https://github.com/CompVis/taming-transformers +-- merci +""" + +import itertools +from contextlib import contextmanager, nullcontext +from functools import partial + +import numpy as np +import pytorch_lightning as pl +import torch +import torch.nn as nn +from einops import rearrange, repeat +from omegaconf import ListConfig +from pytorch_lightning.utilities.rank_zero import rank_zero_only +from torch.optim.lr_scheduler import LambdaLR +from torchvision.utils import make_grid +from tqdm import tqdm + +from extern.ldm_zero123.models.autoencoder import ( + AutoencoderKL, + IdentityFirstStage, + VQModelInterface, +) +from extern.ldm_zero123.models.diffusion.ddim import DDIMSampler +from extern.ldm_zero123.modules.attention import CrossAttention +from extern.ldm_zero123.modules.diffusionmodules.util import ( + extract_into_tensor, + make_beta_schedule, + noise_like, +) +from extern.ldm_zero123.modules.distributions.distributions import ( + DiagonalGaussianDistribution, + normal_kl, +) +from extern.ldm_zero123.modules.ema import LitEma +from extern.ldm_zero123.util import ( + count_params, + default, + exists, + instantiate_from_config, + isimage, + ismap, + log_txt_as_img, + mean_flat, +) + +__conditioning_keys__ = {"concat": "c_concat", "crossattn": "c_crossattn", "adm": "y"} + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +def uniform_on_device(r1, r2, shape, device): + return (r1 - r2) * torch.rand(*shape, device=device) + r2 + + +class DDPM(pl.LightningModule): + # classic DDPM with Gaussian diffusion, in image space + def __init__( + self, + unet_config, + timesteps=1000, + beta_schedule="linear", + loss_type="l2", + ckpt_path=None, + ignore_keys=[], + load_only_unet=False, + monitor="val/loss", + use_ema=True, + first_stage_key="image", + image_size=256, + channels=3, + log_every_t=100, + clip_denoised=True, + linear_start=1e-4, + linear_end=2e-2, + cosine_s=8e-3, + given_betas=None, + original_elbo_weight=0.0, + v_posterior=0.0, # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta + l_simple_weight=1.0, + conditioning_key=None, + parameterization="eps", # all assuming fixed variance schedules + scheduler_config=None, + use_positional_encodings=False, + learn_logvar=False, + logvar_init=0.0, + make_it_fit=False, + ucg_training=None, + ): + super().__init__() + assert parameterization in [ + "eps", + "x0", + ], 'currently only supporting "eps" and "x0"' + self.parameterization = parameterization + print( + f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode" + ) + self.cond_stage_model = None + self.clip_denoised = clip_denoised + self.log_every_t = log_every_t + self.first_stage_key = first_stage_key + self.image_size = image_size # try conv? + self.channels = channels + self.use_positional_encodings = use_positional_encodings + self.model = DiffusionWrapper(unet_config, conditioning_key) + count_params(self.model, verbose=True) + self.use_ema = use_ema + if self.use_ema: + self.model_ema = LitEma(self.model) + print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + self.use_scheduler = scheduler_config is not None + if self.use_scheduler: + self.scheduler_config = scheduler_config + + self.v_posterior = v_posterior + self.original_elbo_weight = original_elbo_weight + self.l_simple_weight = l_simple_weight + + if monitor is not None: + self.monitor = monitor + self.make_it_fit = make_it_fit + if ckpt_path is not None: + self.init_from_ckpt( + ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet + ) + + self.register_schedule( + given_betas=given_betas, + beta_schedule=beta_schedule, + timesteps=timesteps, + linear_start=linear_start, + linear_end=linear_end, + cosine_s=cosine_s, + ) + + self.loss_type = loss_type + + self.learn_logvar = learn_logvar + self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,)) + if self.learn_logvar: + self.logvar = nn.Parameter(self.logvar, requires_grad=True) + + self.ucg_training = ucg_training or dict() + if self.ucg_training: + self.ucg_prng = np.random.RandomState() + + def register_schedule( + self, + given_betas=None, + beta_schedule="linear", + timesteps=1000, + linear_start=1e-4, + linear_end=2e-2, + cosine_s=8e-3, + ): + if exists(given_betas): + betas = given_betas + else: + betas = make_beta_schedule( + beta_schedule, + timesteps, + linear_start=linear_start, + linear_end=linear_end, + cosine_s=cosine_s, + ) + alphas = 1.0 - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1]) + + (timesteps,) = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + assert ( + alphas_cumprod.shape[0] == self.num_timesteps + ), "alphas have to be defined for each timestep" + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer("betas", to_torch(betas)) + self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod)) + self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer( + "sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod)) + ) + self.register_buffer( + "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod)) + ) + self.register_buffer( + "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod)) + ) + self.register_buffer( + "sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1)) + ) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + posterior_variance = (1 - self.v_posterior) * betas * ( + 1.0 - alphas_cumprod_prev + ) / (1.0 - alphas_cumprod) + self.v_posterior * betas + # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) + self.register_buffer("posterior_variance", to_torch(posterior_variance)) + # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain + self.register_buffer( + "posterior_log_variance_clipped", + to_torch(np.log(np.maximum(posterior_variance, 1e-20))), + ) + self.register_buffer( + "posterior_mean_coef1", + to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)), + ) + self.register_buffer( + "posterior_mean_coef2", + to_torch( + (1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod) + ), + ) + + if self.parameterization == "eps": + lvlb_weights = self.betas**2 / ( + 2 + * self.posterior_variance + * to_torch(alphas) + * (1 - self.alphas_cumprod) + ) + elif self.parameterization == "x0": + lvlb_weights = ( + 0.5 + * np.sqrt(torch.Tensor(alphas_cumprod)) + / (2.0 * 1 - torch.Tensor(alphas_cumprod)) + ) + else: + raise NotImplementedError("mu not supported") + # TODO how to choose this term + lvlb_weights[0] = lvlb_weights[1] + self.register_buffer("lvlb_weights", lvlb_weights, persistent=False) + assert not torch.isnan(self.lvlb_weights).all() + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.model.parameters()) + self.model_ema.copy_to(self.model) + if context is not None: + print(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.model.parameters()) + if context is not None: + print(f"{context}: Restored training weights") + + @torch.no_grad() + def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): + sd = torch.load(path, map_location="cpu") + if "state_dict" in list(sd.keys()): + sd = sd["state_dict"] + keys = list(sd.keys()) + + if self.make_it_fit: + n_params = len( + [ + name + for name, _ in itertools.chain( + self.named_parameters(), self.named_buffers() + ) + ] + ) + for name, param in tqdm( + itertools.chain(self.named_parameters(), self.named_buffers()), + desc="Fitting old weights to new weights", + total=n_params, + ): + if not name in sd: + continue + old_shape = sd[name].shape + new_shape = param.shape + assert len(old_shape) == len(new_shape) + if len(new_shape) > 2: + # we only modify first two axes + assert new_shape[2:] == old_shape[2:] + # assumes first axis corresponds to output dim + if not new_shape == old_shape: + new_param = param.clone() + old_param = sd[name] + if len(new_shape) == 1: + for i in range(new_param.shape[0]): + new_param[i] = old_param[i % old_shape[0]] + elif len(new_shape) >= 2: + for i in range(new_param.shape[0]): + for j in range(new_param.shape[1]): + new_param[i, j] = old_param[ + i % old_shape[0], j % old_shape[1] + ] + + n_used_old = torch.ones(old_shape[1]) + for j in range(new_param.shape[1]): + n_used_old[j % old_shape[1]] += 1 + n_used_new = torch.zeros(new_shape[1]) + for j in range(new_param.shape[1]): + n_used_new[j] = n_used_old[j % old_shape[1]] + + n_used_new = n_used_new[None, :] + while len(n_used_new.shape) < len(new_shape): + n_used_new = n_used_new.unsqueeze(-1) + new_param /= n_used_new + + sd[name] = new_param + + missing, unexpected = ( + self.load_state_dict(sd, strict=False) + if not only_model + else self.model.load_state_dict(sd, strict=False) + ) + print( + f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys" + ) + if len(missing) > 0: + print(f"Missing Keys: {missing}") + if len(unexpected) > 0: + print(f"Unexpected Keys: {unexpected}") + + def q_mean_variance(self, x_start, t): + """ + Get the distribution q(x_t | x_0). + :param x_start: the [N x C x ...] tensor of noiseless inputs. + :param t: the number of diffusion steps (minus 1). Here, 0 means one step. + :return: A tuple (mean, variance, log_variance), all of x_start's shape. + """ + mean = extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) + log_variance = extract_into_tensor( + self.log_one_minus_alphas_cumprod, t, x_start.shape + ) + return mean, variance, log_variance + + def predict_start_from_noise(self, x_t, t, noise): + return ( + extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t + - extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) + * noise + ) + + def q_posterior(self, x_start, x_t, t): + posterior_mean = ( + extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t + ) + posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) + posterior_log_variance_clipped = extract_into_tensor( + self.posterior_log_variance_clipped, t, x_t.shape + ) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def p_mean_variance(self, x, t, clip_denoised: bool): + model_out = self.model(x, t) + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + if clip_denoised: + x_recon.clamp_(-1.0, 1.0) + + model_mean, posterior_variance, posterior_log_variance = self.q_posterior( + x_start=x_recon, x_t=x, t=t + ) + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): + b, *_, device = *x.shape, x.device + model_mean, _, model_log_variance = self.p_mean_variance( + x=x, t=t, clip_denoised=clip_denoised + ) + noise = noise_like(x.shape, device, repeat_noise) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def p_sample_loop(self, shape, return_intermediates=False): + device = self.betas.device + b = shape[0] + img = torch.randn(shape, device=device) + intermediates = [img] + for i in tqdm( + reversed(range(0, self.num_timesteps)), + desc="Sampling t", + total=self.num_timesteps, + ): + img = self.p_sample( + img, + torch.full((b,), i, device=device, dtype=torch.long), + clip_denoised=self.clip_denoised, + ) + if i % self.log_every_t == 0 or i == self.num_timesteps - 1: + intermediates.append(img) + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample(self, batch_size=16, return_intermediates=False): + image_size = self.image_size + channels = self.channels + return self.p_sample_loop( + (batch_size, channels, image_size, image_size), + return_intermediates=return_intermediates, + ) + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) + * noise + ) + + def get_loss(self, pred, target, mean=True): + if self.loss_type == "l1": + loss = (target - pred).abs() + if mean: + loss = loss.mean() + elif self.loss_type == "l2": + if mean: + loss = torch.nn.functional.mse_loss(target, pred) + else: + loss = torch.nn.functional.mse_loss(target, pred, reduction="none") + else: + raise NotImplementedError("unknown loss type '{loss_type}'") + + return loss + + def p_losses(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + model_out = self.model(x_noisy, t) + + loss_dict = {} + if self.parameterization == "eps": + target = noise + elif self.parameterization == "x0": + target = x_start + else: + raise NotImplementedError( + f"Paramterization {self.parameterization} not yet supported" + ) + + loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3]) + + log_prefix = "train" if self.training else "val" + + loss_dict.update({f"{log_prefix}/loss_simple": loss.mean()}) + loss_simple = loss.mean() * self.l_simple_weight + + loss_vlb = (self.lvlb_weights[t] * loss).mean() + loss_dict.update({f"{log_prefix}/loss_vlb": loss_vlb}) + + loss = loss_simple + self.original_elbo_weight * loss_vlb + + loss_dict.update({f"{log_prefix}/loss": loss}) + + return loss, loss_dict + + def forward(self, x, *args, **kwargs): + # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size + # assert h == img_size and w == img_size, f'height and width of image must be {img_size}' + t = torch.randint( + 0, self.num_timesteps, (x.shape[0],), device=self.device + ).long() + return self.p_losses(x, t, *args, **kwargs) + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = rearrange(x, "b h w c -> b c h w") + x = x.to(memory_format=torch.contiguous_format).float() + return x + + def shared_step(self, batch): + x = self.get_input(batch, self.first_stage_key) + loss, loss_dict = self(x) + return loss, loss_dict + + def training_step(self, batch, batch_idx): + for k in self.ucg_training: + p = self.ucg_training[k]["p"] + val = self.ucg_training[k]["val"] + if val is None: + val = "" + for i in range(len(batch[k])): + if self.ucg_prng.choice(2, p=[1 - p, p]): + batch[k][i] = val + + loss, loss_dict = self.shared_step(batch) + + self.log_dict( + loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True + ) + + self.log( + "global_step", + self.global_step, + prog_bar=True, + logger=True, + on_step=True, + on_epoch=False, + ) + + if self.use_scheduler: + lr = self.optimizers().param_groups[0]["lr"] + self.log( + "lr_abs", lr, prog_bar=True, logger=True, on_step=True, on_epoch=False + ) + + return loss + + @torch.no_grad() + def validation_step(self, batch, batch_idx): + _, loss_dict_no_ema = self.shared_step(batch) + with self.ema_scope(): + _, loss_dict_ema = self.shared_step(batch) + loss_dict_ema = {key + "_ema": loss_dict_ema[key] for key in loss_dict_ema} + self.log_dict( + loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True + ) + self.log_dict( + loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True + ) + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self.model) + + def _get_rows_from_list(self, samples): + n_imgs_per_row = len(samples) + denoise_grid = rearrange(samples, "n b c h w -> b n c h w") + denoise_grid = rearrange(denoise_grid, "b n c h w -> (b n) c h w") + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): + log = dict() + x = self.get_input(batch, self.first_stage_key) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + x = x.to(self.device)[:N] + log["inputs"] = x + + # get diffusion row + diffusion_row = list() + x_start = x[:n_row] + + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), "1 -> b", b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(x_start) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + diffusion_row.append(x_noisy) + + log["diffusion_row"] = self._get_rows_from_list(diffusion_row) + + if sample: + # get denoise row + with self.ema_scope("Plotting"): + samples, denoise_row = self.sample( + batch_size=N, return_intermediates=True + ) + + log["samples"] = samples + log["denoise_row"] = self._get_rows_from_list(denoise_row) + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + def configure_optimizers(self): + lr = self.learning_rate + params = list(self.model.parameters()) + if self.learn_logvar: + params = params + [self.logvar] + opt = torch.optim.AdamW(params, lr=lr) + return opt + + +class LatentDiffusion(DDPM): + """main class""" + + def __init__( + self, + first_stage_config, + cond_stage_config, + num_timesteps_cond=None, + cond_stage_key="image", + cond_stage_trainable=False, + concat_mode=True, + cond_stage_forward=None, + conditioning_key=None, + scale_factor=1.0, + scale_by_std=False, + unet_trainable=True, + *args, + **kwargs, + ): + self.num_timesteps_cond = default(num_timesteps_cond, 1) + self.scale_by_std = scale_by_std + assert self.num_timesteps_cond <= kwargs["timesteps"] + # for backwards compatibility after implementation of DiffusionWrapper + if conditioning_key is None: + conditioning_key = "concat" if concat_mode else "crossattn" + if cond_stage_config == "__is_unconditional__": + conditioning_key = None + ckpt_path = kwargs.pop("ckpt_path", None) + ignore_keys = kwargs.pop("ignore_keys", []) + super().__init__(conditioning_key=conditioning_key, *args, **kwargs) + self.concat_mode = concat_mode + self.cond_stage_trainable = cond_stage_trainable + self.unet_trainable = unet_trainable + self.cond_stage_key = cond_stage_key + try: + self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 + except: + self.num_downs = 0 + if not scale_by_std: + self.scale_factor = scale_factor + else: + self.register_buffer("scale_factor", torch.tensor(scale_factor)) + self.instantiate_first_stage(first_stage_config) + self.instantiate_cond_stage(cond_stage_config) + self.cond_stage_forward = cond_stage_forward + + # construct linear projection layer for concatenating image CLIP embedding and RT + self.cc_projection = nn.Linear(772, 768) + nn.init.eye_(list(self.cc_projection.parameters())[0][:768, :768]) + nn.init.zeros_(list(self.cc_projection.parameters())[1]) + self.cc_projection.requires_grad_(True) + + self.clip_denoised = False + self.bbox_tokenizer = None + + self.restarted_from_ckpt = False + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys) + self.restarted_from_ckpt = True + + def make_cond_schedule( + self, + ): + self.cond_ids = torch.full( + size=(self.num_timesteps,), + fill_value=self.num_timesteps - 1, + dtype=torch.long, + ) + ids = torch.round( + torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond) + ).long() + self.cond_ids[: self.num_timesteps_cond] = ids + + @rank_zero_only + @torch.no_grad() + def on_train_batch_start(self, batch, batch_idx, dataloader_idx): + # only for very first batch + if ( + self.scale_by_std + and self.current_epoch == 0 + and self.global_step == 0 + and batch_idx == 0 + and not self.restarted_from_ckpt + ): + assert ( + self.scale_factor == 1.0 + ), "rather not use custom rescaling and std-rescaling simultaneously" + # set rescale weight to 1./std of encodings + print("### USING STD-RESCALING ###") + x = super().get_input(batch, self.first_stage_key) + x = x.to(self.device) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + del self.scale_factor + self.register_buffer("scale_factor", 1.0 / z.flatten().std()) + print(f"setting self.scale_factor to {self.scale_factor}") + print("### USING STD-RESCALING ###") + + def register_schedule( + self, + given_betas=None, + beta_schedule="linear", + timesteps=1000, + linear_start=1e-4, + linear_end=2e-2, + cosine_s=8e-3, + ): + super().register_schedule( + given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s + ) + + self.shorten_cond_schedule = self.num_timesteps_cond > 1 + if self.shorten_cond_schedule: + self.make_cond_schedule() + + def instantiate_first_stage(self, config): + model = instantiate_from_config(config) + self.first_stage_model = model.eval() + self.first_stage_model.train = disabled_train + for param in self.first_stage_model.parameters(): + param.requires_grad = False + + def instantiate_cond_stage(self, config): + if not self.cond_stage_trainable: + if config == "__is_first_stage__": + print("Using first stage also as cond stage.") + self.cond_stage_model = self.first_stage_model + elif config == "__is_unconditional__": + print(f"Training {self.__class__.__name__} as an unconditional model.") + self.cond_stage_model = None + # self.be_unconditional = True + else: + model = instantiate_from_config(config) + self.cond_stage_model = model.eval() + self.cond_stage_model.train = disabled_train + for param in self.cond_stage_model.parameters(): + param.requires_grad = False + else: + assert config != "__is_first_stage__" + assert config != "__is_unconditional__" + model = instantiate_from_config(config) + self.cond_stage_model = model + + def _get_denoise_row_from_list( + self, samples, desc="", force_no_decoder_quantization=False + ): + denoise_row = [] + for zd in tqdm(samples, desc=desc): + denoise_row.append( + self.decode_first_stage( + zd.to(self.device), force_not_quantize=force_no_decoder_quantization + ) + ) + n_imgs_per_row = len(denoise_row) + denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W + denoise_grid = rearrange(denoise_row, "n b c h w -> b n c h w") + denoise_grid = rearrange(denoise_grid, "b n c h w -> (b n) c h w") + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + def get_first_stage_encoding(self, encoder_posterior): + if isinstance(encoder_posterior, DiagonalGaussianDistribution): + z = encoder_posterior.sample() + elif isinstance(encoder_posterior, torch.Tensor): + z = encoder_posterior + else: + raise NotImplementedError( + f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented" + ) + return self.scale_factor * z + + def get_learned_conditioning(self, c): + if self.cond_stage_forward is None: + if hasattr(self.cond_stage_model, "encode") and callable( + self.cond_stage_model.encode + ): + c = self.cond_stage_model.encode(c) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + else: + c = self.cond_stage_model(c) + else: + assert hasattr(self.cond_stage_model, self.cond_stage_forward) + c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) + return c + + def meshgrid(self, h, w): + y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) + x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) + + arr = torch.cat([y, x], dim=-1) + return arr + + def delta_border(self, h, w): + """ + :param h: height + :param w: width + :return: normalized distance to image border, + wtith min distance = 0 at border and max dist = 0.5 at image center + """ + lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) + arr = self.meshgrid(h, w) / lower_right_corner + dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] + dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] + edge_dist = torch.min( + torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1 + )[0] + return edge_dist + + def get_weighting(self, h, w, Ly, Lx, device): + weighting = self.delta_border(h, w) + weighting = torch.clip( + weighting, + self.split_input_params["clip_min_weight"], + self.split_input_params["clip_max_weight"], + ) + weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) + + if self.split_input_params["tie_braker"]: + L_weighting = self.delta_border(Ly, Lx) + L_weighting = torch.clip( + L_weighting, + self.split_input_params["clip_min_tie_weight"], + self.split_input_params["clip_max_tie_weight"], + ) + + L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) + weighting = weighting * L_weighting + return weighting + + def get_fold_unfold( + self, x, kernel_size, stride, uf=1, df=1 + ): # todo load once not every time, shorten code + """ + :param x: img of size (bs, c, h, w) + :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) + """ + bs, nc, h, w = x.shape + + # number of crops in image + Ly = (h - kernel_size[0]) // stride[0] + 1 + Lx = (w - kernel_size[1]) // stride[1] + 1 + + if uf == 1 and df == 1: + fold_params = dict( + kernel_size=kernel_size, dilation=1, padding=0, stride=stride + ) + unfold = torch.nn.Unfold(**fold_params) + + fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) + + weighting = self.get_weighting( + kernel_size[0], kernel_size[1], Ly, Lx, x.device + ).to(x.dtype) + normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) + + elif uf > 1 and df == 1: + fold_params = dict( + kernel_size=kernel_size, dilation=1, padding=0, stride=stride + ) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict( + kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), + dilation=1, + padding=0, + stride=(stride[0] * uf, stride[1] * uf), + ) + fold = torch.nn.Fold( + output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2 + ) + + weighting = self.get_weighting( + kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device + ).to(x.dtype) + normalization = fold(weighting).view( + 1, 1, h * uf, w * uf + ) # normalizes the overlap + weighting = weighting.view( + (1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx) + ) + + elif df > 1 and uf == 1: + fold_params = dict( + kernel_size=kernel_size, dilation=1, padding=0, stride=stride + ) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict( + kernel_size=(kernel_size[0] // df, kernel_size[0] // df), + dilation=1, + padding=0, + stride=(stride[0] // df, stride[1] // df), + ) + fold = torch.nn.Fold( + output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2 + ) + + weighting = self.get_weighting( + kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device + ).to(x.dtype) + normalization = fold(weighting).view( + 1, 1, h // df, w // df + ) # normalizes the overlap + weighting = weighting.view( + (1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx) + ) + + else: + raise NotImplementedError + + return fold, unfold, normalization, weighting + + @torch.no_grad() + def get_input( + self, + batch, + k, + return_first_stage_outputs=False, + force_c_encode=False, + cond_key=None, + return_original_cond=False, + bs=None, + uncond=0.05, + ): + x = super().get_input(batch, k) + T = batch["T"].to(memory_format=torch.contiguous_format).float() + + if bs is not None: + x = x[:bs] + T = T[:bs].to(self.device) + + x = x.to(self.device) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + cond_key = cond_key or self.cond_stage_key + xc = super().get_input(batch, cond_key).to(self.device) + if bs is not None: + xc = xc[:bs] + cond = {} + + # To support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%. + random = torch.rand(x.size(0), device=x.device) + prompt_mask = rearrange(random < 2 * uncond, "n -> n 1 1") + input_mask = 1 - rearrange( + (random >= uncond).float() * (random < 3 * uncond).float(), "n -> n 1 1 1" + ) + null_prompt = self.get_learned_conditioning([""]) + + # z.shape: [8, 4, 64, 64]; c.shape: [8, 1, 768] + # print('=========== xc shape ===========', xc.shape) + with torch.enable_grad(): + clip_emb = self.get_learned_conditioning(xc).detach() + null_prompt = self.get_learned_conditioning([""]).detach() + cond["c_crossattn"] = [ + self.cc_projection( + torch.cat( + [ + torch.where(prompt_mask, null_prompt, clip_emb), + T[:, None, :], + ], + dim=-1, + ) + ) + ] + cond["c_concat"] = [ + input_mask * self.encode_first_stage((xc.to(self.device))).mode().detach() + ] + out = [z, cond] + if return_first_stage_outputs: + xrec = self.decode_first_stage(z) + out.extend([x, xrec]) + if return_original_cond: + out.append(xc) + return out + + # @torch.no_grad() + def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): + if predict_cids: + if z.dim() == 4: + z = torch.argmax(z.exp(), dim=1).long() + z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) + z = rearrange(z, "b h w c -> b c h w").contiguous() + + z = 1.0 / self.scale_factor * z + + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + uf = self.split_input_params["vqf"] + bs, nc, h, w = z.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold( + z, ks, stride, uf=uf + ) + + z = unfold(z) # (bn, nc * prod(**ks), L) + # 1. Reshape to img shape + z = z.view( + (z.shape[0], -1, ks[0], ks[1], z.shape[-1]) + ) # (bn, nc, ks[0], ks[1], L ) + + # 2. apply model loop over last dim + if isinstance(self.first_stage_model, VQModelInterface): + output_list = [ + self.first_stage_model.decode( + z[:, :, :, :, i], + force_not_quantize=predict_cids or force_not_quantize, + ) + for i in range(z.shape[-1]) + ] + else: + output_list = [ + self.first_stage_model.decode(z[:, :, :, :, i]) + for i in range(z.shape[-1]) + ] + + o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) + o = o * weighting + # Reverse 1. reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization # norm is shape (1, 1, h, w) + return decoded + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode( + z, force_not_quantize=predict_cids or force_not_quantize + ) + else: + return self.first_stage_model.decode(z) + + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode( + z, force_not_quantize=predict_cids or force_not_quantize + ) + else: + return self.first_stage_model.decode(z) + + # @torch.no_grad() # wasted two hours to find this bug... why no grad here! + def encode_first_stage(self, x): + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + df = self.split_input_params["vqf"] + self.split_input_params["original_image_size"] = x.shape[-2:] + bs, nc, h, w = x.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold( + x, ks, stride, df=df + ) + z = unfold(x) # (bn, nc * prod(**ks), L) + # Reshape to img shape + z = z.view( + (z.shape[0], -1, ks[0], ks[1], z.shape[-1]) + ) # (bn, nc, ks[0], ks[1], L ) + + output_list = [ + self.first_stage_model.encode(z[:, :, :, :, i]) + for i in range(z.shape[-1]) + ] + + o = torch.stack(output_list, axis=-1) + o = o * weighting + + # Reverse reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization + return decoded + + else: + return self.first_stage_model.encode(x) + else: + return self.first_stage_model.encode(x) + + def shared_step(self, batch, **kwargs): + x, c = self.get_input(batch, self.first_stage_key) + loss = self(x, c) + return loss + + def forward(self, x, c, *args, **kwargs): + t = torch.randint( + 0, self.num_timesteps, (x.shape[0],), device=self.device + ).long() + if self.model.conditioning_key is not None: + assert c is not None + # if self.cond_stage_trainable: + # c = self.get_learned_conditioning(c) + if self.shorten_cond_schedule: # TODO: drop this option + tc = self.cond_ids[t].to(self.device) + c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) + return self.p_losses(x, c, t, *args, **kwargs) + + def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset + def rescale_bbox(bbox): + x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2]) + y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3]) + w = min(bbox[2] / crop_coordinates[2], 1 - x0) + h = min(bbox[3] / crop_coordinates[3], 1 - y0) + return x0, y0, w, h + + return [rescale_bbox(b) for b in bboxes] + + def apply_model(self, x_noisy, t, cond, return_ids=False): + if isinstance(cond, dict): + # hybrid case, cond is exptected to be a dict + pass + else: + if not isinstance(cond, list): + cond = [cond] + key = ( + "c_concat" if self.model.conditioning_key == "concat" else "c_crossattn" + ) + cond = {key: cond} + + if hasattr(self, "split_input_params"): + assert len(cond) == 1 # todo can only deal with one conditioning atm + assert not return_ids + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + + h, w = x_noisy.shape[-2:] + + fold, unfold, normalization, weighting = self.get_fold_unfold( + x_noisy, ks, stride + ) + + z = unfold(x_noisy) # (bn, nc * prod(**ks), L) + # Reshape to img shape + z = z.view( + (z.shape[0], -1, ks[0], ks[1], z.shape[-1]) + ) # (bn, nc, ks[0], ks[1], L ) + z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])] + + if ( + self.cond_stage_key in ["image", "LR_image", "segmentation", "bbox_img"] + and self.model.conditioning_key + ): # todo check for completeness + c_key = next(iter(cond.keys())) # get key + c = next(iter(cond.values())) # get value + assert len(c) == 1 # todo extend to list with more than one elem + c = c[0] # get element + + c = unfold(c) + c = c.view( + (c.shape[0], -1, ks[0], ks[1], c.shape[-1]) + ) # (bn, nc, ks[0], ks[1], L ) + + cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])] + + elif self.cond_stage_key == "coordinates_bbox": + assert ( + "original_image_size" in self.split_input_params + ), "BoudingBoxRescaling is missing original_image_size" + + # assuming padding of unfold is always 0 and its dilation is always 1 + n_patches_per_row = int((w - ks[0]) / stride[0] + 1) + full_img_h, full_img_w = self.split_input_params["original_image_size"] + # as we are operating on latents, we need the factor from the original image size to the + # spatial latent size to properly rescale the crops for regenerating the bbox annotations + num_downs = self.first_stage_model.encoder.num_resolutions - 1 + rescale_latent = 2 ** (num_downs) + + # get top left postions of patches as conforming for the bbbox tokenizer, therefore we + # need to rescale the tl patch coordinates to be in between (0,1) + tl_patch_coordinates = [ + ( + rescale_latent + * stride[0] + * (patch_nr % n_patches_per_row) + / full_img_w, + rescale_latent + * stride[1] + * (patch_nr // n_patches_per_row) + / full_img_h, + ) + for patch_nr in range(z.shape[-1]) + ] + + # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w) + patch_limits = [ + ( + x_tl, + y_tl, + rescale_latent * ks[0] / full_img_w, + rescale_latent * ks[1] / full_img_h, + ) + for x_tl, y_tl in tl_patch_coordinates + ] + # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates] + + # tokenize crop coordinates for the bounding boxes of the respective patches + patch_limits_tknzd = [ + torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to( + self.device + ) + for bbox in patch_limits + ] # list of length l with tensors of shape (1, 2) + # cut tknzd crop position from conditioning + assert isinstance(cond, dict), "cond must be dict to be fed into model" + cut_cond = cond["c_crossattn"][0][..., :-2].to(self.device) + + adapted_cond = torch.stack( + [torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd] + ) + adapted_cond = rearrange(adapted_cond, "l b n -> (l b) n") + adapted_cond = self.get_learned_conditioning(adapted_cond) + adapted_cond = rearrange( + adapted_cond, "(l b) n d -> l b n d", l=z.shape[-1] + ) + + cond_list = [{"c_crossattn": [e]} for e in adapted_cond] + + else: + cond_list = [ + cond for i in range(z.shape[-1]) + ] # Todo make this more efficient + + # apply model by loop over crops + output_list = [ + self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1]) + ] + assert not isinstance( + output_list[0], tuple + ) # todo cant deal with multiple model outputs check this never happens + + o = torch.stack(output_list, axis=-1) + o = o * weighting + # Reverse reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + x_recon = fold(o) / normalization + + else: + x_recon = self.model(x_noisy, t, **cond) + + if isinstance(x_recon, tuple) and not return_ids: + return x_recon[0] + else: + return x_recon + + def _predict_eps_from_xstart(self, x_t, t, pred_xstart): + return ( + extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t + - pred_xstart + ) / extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) + + def _prior_bpd(self, x_start): + """ + Get the prior KL term for the variational lower-bound, measured in + bits-per-dim. + This term can't be optimized, as it only depends on the encoder. + :param x_start: the [N x C x ...] tensor of inputs. + :return: a batch of [N] KL values (in bits), one per batch element. + """ + batch_size = x_start.shape[0] + t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) + qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) + kl_prior = normal_kl( + mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0 + ) + return mean_flat(kl_prior) / np.log(2.0) + + def p_losses(self, x_start, cond, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + model_output = self.apply_model(x_noisy, t, cond) + + loss_dict = {} + prefix = "train" if self.training else "val" + + if self.parameterization == "x0": + target = x_start + elif self.parameterization == "eps": + target = noise + else: + raise NotImplementedError() + + loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) + loss_dict.update({f"{prefix}/loss_simple": loss_simple.mean()}) + + logvar_t = self.logvar[t].to(self.device) + loss = loss_simple / torch.exp(logvar_t) + logvar_t + # loss = loss_simple / torch.exp(self.logvar) + self.logvar + if self.learn_logvar: + loss_dict.update({f"{prefix}/loss_gamma": loss.mean()}) + loss_dict.update({"logvar": self.logvar.data.mean()}) + + loss = self.l_simple_weight * loss.mean() + + loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) + loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() + loss_dict.update({f"{prefix}/loss_vlb": loss_vlb}) + loss += self.original_elbo_weight * loss_vlb + loss_dict.update({f"{prefix}/loss": loss}) + + return loss, loss_dict + + def p_mean_variance( + self, + x, + c, + t, + clip_denoised: bool, + return_codebook_ids=False, + quantize_denoised=False, + return_x0=False, + score_corrector=None, + corrector_kwargs=None, + ): + t_in = t + model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) + + if score_corrector is not None: + assert self.parameterization == "eps" + model_out = score_corrector.modify_score( + self, model_out, x, t, c, **corrector_kwargs + ) + + if return_codebook_ids: + model_out, logits = model_out + + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + else: + raise NotImplementedError() + + if clip_denoised: + x_recon.clamp_(-1.0, 1.0) + if quantize_denoised: + x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) + model_mean, posterior_variance, posterior_log_variance = self.q_posterior( + x_start=x_recon, x_t=x, t=t + ) + if return_codebook_ids: + return model_mean, posterior_variance, posterior_log_variance, logits + elif return_x0: + return model_mean, posterior_variance, posterior_log_variance, x_recon + else: + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample( + self, + x, + c, + t, + clip_denoised=False, + repeat_noise=False, + return_codebook_ids=False, + quantize_denoised=False, + return_x0=False, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + ): + b, *_, device = *x.shape, x.device + outputs = self.p_mean_variance( + x=x, + c=c, + t=t, + clip_denoised=clip_denoised, + return_codebook_ids=return_codebook_ids, + quantize_denoised=quantize_denoised, + return_x0=return_x0, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + ) + if return_codebook_ids: + raise DeprecationWarning("Support dropped.") + model_mean, _, model_log_variance, logits = outputs + elif return_x0: + model_mean, _, model_log_variance, x0 = outputs + else: + model_mean, _, model_log_variance = outputs + + noise = noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.0: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + + if return_codebook_ids: + return model_mean + nonzero_mask * ( + 0.5 * model_log_variance + ).exp() * noise, logits.argmax(dim=1) + if return_x0: + return ( + model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, + x0, + ) + else: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def progressive_denoising( + self, + cond, + shape, + verbose=True, + callback=None, + quantize_denoised=False, + img_callback=None, + mask=None, + x0=None, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + batch_size=None, + x_T=None, + start_T=None, + log_every_t=None, + ): + if not log_every_t: + log_every_t = self.log_every_t + timesteps = self.num_timesteps + if batch_size is not None: + b = batch_size if batch_size is not None else shape[0] + shape = [batch_size] + list(shape) + else: + b = batch_size = shape[0] + if x_T is None: + img = torch.randn(shape, device=self.device) + else: + img = x_T + intermediates = [] + if cond is not None: + if isinstance(cond, dict): + cond = { + key: cond[key][:batch_size] + if not isinstance(cond[key], list) + else list(map(lambda x: x[:batch_size], cond[key])) + for key in cond + } + else: + cond = ( + [c[:batch_size] for c in cond] + if isinstance(cond, list) + else cond[:batch_size] + ) + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = ( + tqdm( + reversed(range(0, timesteps)), + desc="Progressive Generation", + total=timesteps, + ) + if verbose + else reversed(range(0, timesteps)) + ) + if type(temperature) == float: + temperature = [temperature] * timesteps + + for i in iterator: + ts = torch.full((b,), i, device=self.device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != "hybrid" + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img, x0_partial = self.p_sample( + img, + cond, + ts, + clip_denoised=self.clip_denoised, + quantize_denoised=quantize_denoised, + return_x0=True, + temperature=temperature[i], + noise_dropout=noise_dropout, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + ) + if mask is not None: + assert x0 is not None + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1.0 - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(x0_partial) + if callback: + callback(i) + if img_callback: + img_callback(img, i) + return img, intermediates + + @torch.no_grad() + def p_sample_loop( + self, + cond, + shape, + return_intermediates=False, + x_T=None, + verbose=True, + callback=None, + timesteps=None, + quantize_denoised=False, + mask=None, + x0=None, + img_callback=None, + start_T=None, + log_every_t=None, + ): + if not log_every_t: + log_every_t = self.log_every_t + device = self.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + intermediates = [img] + if timesteps is None: + timesteps = self.num_timesteps + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = ( + tqdm(reversed(range(0, timesteps)), desc="Sampling t", total=timesteps) + if verbose + else reversed(range(0, timesteps)) + ) + + if mask is not None: + assert x0 is not None + assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match + + for i in iterator: + ts = torch.full((b,), i, device=device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != "hybrid" + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img = self.p_sample( + img, + cond, + ts, + clip_denoised=self.clip_denoised, + quantize_denoised=quantize_denoised, + ) + if mask is not None: + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1.0 - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(img) + if callback: + callback(i) + if img_callback: + img_callback(img, i) + + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample( + self, + cond, + batch_size=16, + return_intermediates=False, + x_T=None, + verbose=True, + timesteps=None, + quantize_denoised=False, + mask=None, + x0=None, + shape=None, + **kwargs, + ): + if shape is None: + shape = (batch_size, self.channels, self.image_size, self.image_size) + if cond is not None: + if isinstance(cond, dict): + cond = { + key: cond[key][:batch_size] + if not isinstance(cond[key], list) + else list(map(lambda x: x[:batch_size], cond[key])) + for key in cond + } + else: + cond = ( + [c[:batch_size] for c in cond] + if isinstance(cond, list) + else cond[:batch_size] + ) + return self.p_sample_loop( + cond, + shape, + return_intermediates=return_intermediates, + x_T=x_T, + verbose=verbose, + timesteps=timesteps, + quantize_denoised=quantize_denoised, + mask=mask, + x0=x0, + ) + + @torch.no_grad() + def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs): + if ddim: + ddim_sampler = DDIMSampler(self) + shape = (self.channels, self.image_size, self.image_size) + samples, intermediates = ddim_sampler.sample( + ddim_steps, batch_size, shape, cond, verbose=False, **kwargs + ) + + else: + samples, intermediates = self.sample( + cond=cond, batch_size=batch_size, return_intermediates=True, **kwargs + ) + + return samples, intermediates + + @torch.no_grad() + def get_unconditional_conditioning( + self, batch_size, null_label=None, image_size=512 + ): + if null_label is not None: + xc = null_label + if isinstance(xc, ListConfig): + xc = list(xc) + if isinstance(xc, dict) or isinstance(xc, list): + c = self.get_learned_conditioning(xc) + else: + if hasattr(xc, "to"): + xc = xc.to(self.device) + c = self.get_learned_conditioning(xc) + else: + # todo: get null label from cond_stage_model + raise NotImplementedError() + c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device) + cond = {} + cond["c_crossattn"] = [c] + cond["c_concat"] = [ + torch.zeros([batch_size, 4, image_size // 8, image_size // 8]).to( + self.device + ) + ] + return cond + + @torch.no_grad() + def log_images( + self, + batch, + N=8, + n_row=4, + sample=True, + ddim_steps=200, + ddim_eta=1.0, + return_keys=None, + quantize_denoised=True, + inpaint=True, + plot_denoise_rows=False, + plot_progressive_rows=True, + plot_diffusion_rows=True, + unconditional_guidance_scale=1.0, + unconditional_guidance_label=None, + use_ema_scope=True, + **kwargs, + ): + ema_scope = self.ema_scope if use_ema_scope else nullcontext + use_ddim = ddim_steps is not None + + log = dict() + z, c, x, xrec, xc = self.get_input( + batch, + self.first_stage_key, + return_first_stage_outputs=True, + force_c_encode=True, + return_original_cond=True, + bs=N, + ) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + log["inputs"] = x + log["reconstruction"] = xrec + if self.model.conditioning_key is not None: + if hasattr(self.cond_stage_model, "decode"): + xc = self.cond_stage_model.decode(c) + log["conditioning"] = xc + elif self.cond_stage_key in ["caption", "txt"]: + xc = log_txt_as_img( + (x.shape[2], x.shape[3]), + batch[self.cond_stage_key], + size=x.shape[2] // 25, + ) + log["conditioning"] = xc + elif self.cond_stage_key == "class_label": + xc = log_txt_as_img( + (x.shape[2], x.shape[3]), + batch["human_label"], + size=x.shape[2] // 25, + ) + log["conditioning"] = xc + elif isimage(xc): + log["conditioning"] = xc + if ismap(xc): + log["original_conditioning"] = self.to_rgb(xc) + + if plot_diffusion_rows: + # get diffusion row + diffusion_row = list() + z_start = z[:n_row] + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), "1 -> b", b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(z_start) + z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) + diffusion_row.append(self.decode_first_stage(z_noisy)) + + diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W + diffusion_grid = rearrange(diffusion_row, "n b c h w -> b n c h w") + diffusion_grid = rearrange(diffusion_grid, "b n c h w -> (b n) c h w") + diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) + log["diffusion_row"] = diffusion_grid + + if sample: + # get denoise row + with ema_scope("Sampling"): + samples, z_denoise_row = self.sample_log( + cond=c, + batch_size=N, + ddim=use_ddim, + ddim_steps=ddim_steps, + eta=ddim_eta, + ) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + if plot_denoise_rows: + denoise_grid = self._get_denoise_row_from_list(z_denoise_row) + log["denoise_row"] = denoise_grid + + if ( + quantize_denoised + and not isinstance(self.first_stage_model, AutoencoderKL) + and not isinstance(self.first_stage_model, IdentityFirstStage) + ): + # also display when quantizing x0 while sampling + with ema_scope("Plotting Quantized Denoised"): + samples, z_denoise_row = self.sample_log( + cond=c, + batch_size=N, + ddim=use_ddim, + ddim_steps=ddim_steps, + eta=ddim_eta, + quantize_denoised=True, + ) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, + # quantize_denoised=True) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_x0_quantized"] = x_samples + + if unconditional_guidance_scale > 1.0: + uc = self.get_unconditional_conditioning( + N, unconditional_guidance_label, image_size=x.shape[-1] + ) + # uc = torch.zeros_like(c) + with ema_scope("Sampling with classifier-free guidance"): + samples_cfg, _ = self.sample_log( + cond=c, + batch_size=N, + ddim=use_ddim, + ddim_steps=ddim_steps, + eta=ddim_eta, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=uc, + ) + x_samples_cfg = self.decode_first_stage(samples_cfg) + log[ + f"samples_cfg_scale_{unconditional_guidance_scale:.2f}" + ] = x_samples_cfg + + if inpaint: + # make a simple center square + b, h, w = z.shape[0], z.shape[2], z.shape[3] + mask = torch.ones(N, h, w).to(self.device) + # zeros will be filled in + mask[:, h // 4 : 3 * h // 4, w // 4 : 3 * w // 4] = 0.0 + mask = mask[:, None, ...] + with ema_scope("Plotting Inpaint"): + samples, _ = self.sample_log( + cond=c, + batch_size=N, + ddim=use_ddim, + eta=ddim_eta, + ddim_steps=ddim_steps, + x0=z[:N], + mask=mask, + ) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_inpainting"] = x_samples + log["mask"] = mask + + # outpaint + mask = 1.0 - mask + with ema_scope("Plotting Outpaint"): + samples, _ = self.sample_log( + cond=c, + batch_size=N, + ddim=use_ddim, + eta=ddim_eta, + ddim_steps=ddim_steps, + x0=z[:N], + mask=mask, + ) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_outpainting"] = x_samples + + if plot_progressive_rows: + with ema_scope("Plotting Progressives"): + img, progressives = self.progressive_denoising( + c, + shape=(self.channels, self.image_size, self.image_size), + batch_size=N, + ) + prog_row = self._get_denoise_row_from_list( + progressives, desc="Progressive Generation" + ) + log["progressive_row"] = prog_row + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + def configure_optimizers(self): + lr = self.learning_rate + params = [] + if self.unet_trainable == "attn": + print("Training only unet attention layers") + for n, m in self.model.named_modules(): + if isinstance(m, CrossAttention) and n.endswith("attn2"): + params.extend(m.parameters()) + if self.unet_trainable == "conv_in": + print("Training only unet input conv layers") + params = list(self.model.diffusion_model.input_blocks[0][0].parameters()) + elif self.unet_trainable is True or self.unet_trainable == "all": + print("Training the full unet") + params = list(self.model.parameters()) + else: + raise ValueError( + f"Unrecognised setting for unet_trainable: {self.unet_trainable}" + ) + + if self.cond_stage_trainable: + print(f"{self.__class__.__name__}: Also optimizing conditioner params!") + params = params + list(self.cond_stage_model.parameters()) + if self.learn_logvar: + print("Diffusion model optimizing logvar") + params.append(self.logvar) + + if self.cc_projection is not None: + params = params + list(self.cc_projection.parameters()) + print("========== optimizing for cc projection weight ==========") + + opt = torch.optim.AdamW( + [ + {"params": self.model.parameters(), "lr": lr}, + {"params": self.cc_projection.parameters(), "lr": 10.0 * lr}, + ], + lr=lr, + ) + if self.use_scheduler: + assert "target" in self.scheduler_config + scheduler = instantiate_from_config(self.scheduler_config) + + print("Setting up LambdaLR scheduler...") + scheduler = [ + { + "scheduler": LambdaLR(opt, lr_lambda=scheduler.schedule), + "interval": "step", + "frequency": 1, + } + ] + return [opt], scheduler + return opt + + @torch.no_grad() + def to_rgb(self, x): + x = x.float() + if not hasattr(self, "colorize"): + self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) + x = nn.functional.conv2d(x, weight=self.colorize) + x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0 + return x + + +class DiffusionWrapper(pl.LightningModule): + def __init__(self, diff_model_config, conditioning_key): + super().__init__() + self.diffusion_model = instantiate_from_config(diff_model_config) + self.conditioning_key = conditioning_key + assert self.conditioning_key in [ + None, + "concat", + "crossattn", + "hybrid", + "adm", + "hybrid-adm", + ] + + def forward( + self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None + ): + if self.conditioning_key is None: + out = self.diffusion_model(x, t) + elif self.conditioning_key == "concat": + xc = torch.cat([x] + c_concat, dim=1) + out = self.diffusion_model(xc, t) + elif self.conditioning_key == "crossattn": + # c_crossattn dimension: torch.Size([8, 1, 768]) 1 + # cc dimension: torch.Size([8, 1, 768] + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(x, t, context=cc) + elif self.conditioning_key == "hybrid": + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc) + elif self.conditioning_key == "hybrid-adm": + assert c_adm is not None + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc, y=c_adm) + elif self.conditioning_key == "adm": + cc = c_crossattn[0] + out = self.diffusion_model(x, t, y=cc) + else: + raise NotImplementedError() + + return out + + +class LatentUpscaleDiffusion(LatentDiffusion): + def __init__(self, *args, low_scale_config, low_scale_key="LR", **kwargs): + super().__init__(*args, **kwargs) + # assumes that neither the cond_stage nor the low_scale_model contain trainable params + assert not self.cond_stage_trainable + self.instantiate_low_stage(low_scale_config) + self.low_scale_key = low_scale_key + + def instantiate_low_stage(self, config): + model = instantiate_from_config(config) + self.low_scale_model = model.eval() + self.low_scale_model.train = disabled_train + for param in self.low_scale_model.parameters(): + param.requires_grad = False + + @torch.no_grad() + def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False): + if not log_mode: + z, c = super().get_input(batch, k, force_c_encode=True, bs=bs) + else: + z, c, x, xrec, xc = super().get_input( + batch, + self.first_stage_key, + return_first_stage_outputs=True, + force_c_encode=True, + return_original_cond=True, + bs=bs, + ) + x_low = batch[self.low_scale_key][:bs] + x_low = rearrange(x_low, "b h w c -> b c h w") + x_low = x_low.to(memory_format=torch.contiguous_format).float() + zx, noise_level = self.low_scale_model(x_low) + all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level} + # import pudb; pu.db + if log_mode: + # TODO: maybe disable if too expensive + interpretability = False + if interpretability: + zx = zx[:, :, ::2, ::2] + x_low_rec = self.low_scale_model.decode(zx) + return z, all_conds, x, xrec, xc, x_low, x_low_rec, noise_level + return z, all_conds + + @torch.no_grad() + def log_images( + self, + batch, + N=8, + n_row=4, + sample=True, + ddim_steps=200, + ddim_eta=1.0, + return_keys=None, + plot_denoise_rows=False, + plot_progressive_rows=True, + plot_diffusion_rows=True, + unconditional_guidance_scale=1.0, + unconditional_guidance_label=None, + use_ema_scope=True, + **kwargs, + ): + ema_scope = self.ema_scope if use_ema_scope else nullcontext + use_ddim = ddim_steps is not None + + log = dict() + z, c, x, xrec, xc, x_low, x_low_rec, noise_level = self.get_input( + batch, self.first_stage_key, bs=N, log_mode=True + ) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + log["inputs"] = x + log["reconstruction"] = xrec + log["x_lr"] = x_low + log[ + f"x_lr_rec_@noise_levels{'-'.join(map(lambda x: str(x), list(noise_level.cpu().numpy())))}" + ] = x_low_rec + if self.model.conditioning_key is not None: + if hasattr(self.cond_stage_model, "decode"): + xc = self.cond_stage_model.decode(c) + log["conditioning"] = xc + elif self.cond_stage_key in ["caption", "txt"]: + xc = log_txt_as_img( + (x.shape[2], x.shape[3]), + batch[self.cond_stage_key], + size=x.shape[2] // 25, + ) + log["conditioning"] = xc + elif self.cond_stage_key == "class_label": + xc = log_txt_as_img( + (x.shape[2], x.shape[3]), + batch["human_label"], + size=x.shape[2] // 25, + ) + log["conditioning"] = xc + elif isimage(xc): + log["conditioning"] = xc + if ismap(xc): + log["original_conditioning"] = self.to_rgb(xc) + + if plot_diffusion_rows: + # get diffusion row + diffusion_row = list() + z_start = z[:n_row] + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), "1 -> b", b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(z_start) + z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) + diffusion_row.append(self.decode_first_stage(z_noisy)) + + diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W + diffusion_grid = rearrange(diffusion_row, "n b c h w -> b n c h w") + diffusion_grid = rearrange(diffusion_grid, "b n c h w -> (b n) c h w") + diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) + log["diffusion_row"] = diffusion_grid + + if sample: + # get denoise row + with ema_scope("Sampling"): + samples, z_denoise_row = self.sample_log( + cond=c, + batch_size=N, + ddim=use_ddim, + ddim_steps=ddim_steps, + eta=ddim_eta, + ) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + if plot_denoise_rows: + denoise_grid = self._get_denoise_row_from_list(z_denoise_row) + log["denoise_row"] = denoise_grid + + if unconditional_guidance_scale > 1.0: + uc_tmp = self.get_unconditional_conditioning( + N, unconditional_guidance_label + ) + # TODO explore better "unconditional" choices for the other keys + # maybe guide away from empty text label and highest noise level and maximally degraded zx? + uc = dict() + for k in c: + if k == "c_crossattn": + assert isinstance(c[k], list) and len(c[k]) == 1 + uc[k] = [uc_tmp] + elif k == "c_adm": # todo: only run with text-based guidance? + assert isinstance(c[k], torch.Tensor) + uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level + elif isinstance(c[k], list): + uc[k] = [c[k][i] for i in range(len(c[k]))] + else: + uc[k] = c[k] + + with ema_scope("Sampling with classifier-free guidance"): + samples_cfg, _ = self.sample_log( + cond=c, + batch_size=N, + ddim=use_ddim, + ddim_steps=ddim_steps, + eta=ddim_eta, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=uc, + ) + x_samples_cfg = self.decode_first_stage(samples_cfg) + log[ + f"samples_cfg_scale_{unconditional_guidance_scale:.2f}" + ] = x_samples_cfg + + if plot_progressive_rows: + with ema_scope("Plotting Progressives"): + img, progressives = self.progressive_denoising( + c, + shape=(self.channels, self.image_size, self.image_size), + batch_size=N, + ) + prog_row = self._get_denoise_row_from_list( + progressives, desc="Progressive Generation" + ) + log["progressive_row"] = prog_row + + return log + + +class LatentInpaintDiffusion(LatentDiffusion): + """ + can either run as pure inpainting model (only concat mode) or with mixed conditionings, + e.g. mask as concat and text via cross-attn. + To disable finetuning mode, set finetune_keys to None + """ + + def __init__( + self, + finetune_keys=( + "model.diffusion_model.input_blocks.0.0.weight", + "model_ema.diffusion_modelinput_blocks00weight", + ), + concat_keys=("mask", "masked_image"), + masked_image_key="masked_image", + keep_finetune_dims=4, # if model was trained without concat mode before and we would like to keep these channels + c_concat_log_start=None, # to log reconstruction of c_concat codes + c_concat_log_end=None, + *args, + **kwargs, + ): + ckpt_path = kwargs.pop("ckpt_path", None) + ignore_keys = kwargs.pop("ignore_keys", list()) + super().__init__(*args, **kwargs) + self.masked_image_key = masked_image_key + assert self.masked_image_key in concat_keys + self.finetune_keys = finetune_keys + self.concat_keys = concat_keys + self.keep_dims = keep_finetune_dims + self.c_concat_log_start = c_concat_log_start + self.c_concat_log_end = c_concat_log_end + if exists(self.finetune_keys): + assert exists(ckpt_path), "can only finetune from a given checkpoint" + if exists(ckpt_path): + self.init_from_ckpt(ckpt_path, ignore_keys) + + def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): + sd = torch.load(path, map_location="cpu") + if "state_dict" in list(sd.keys()): + sd = sd["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + + # make it explicit, finetune by including extra input channels + if exists(self.finetune_keys) and k in self.finetune_keys: + new_entry = None + for name, param in self.named_parameters(): + if name in self.finetune_keys: + print( + f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only" + ) + new_entry = torch.zeros_like(param) # zero init + assert exists(new_entry), "did not find matching parameter to modify" + new_entry[:, : self.keep_dims, ...] = sd[k] + sd[k] = new_entry + + missing, unexpected = ( + self.load_state_dict(sd, strict=False) + if not only_model + else self.model.load_state_dict(sd, strict=False) + ) + print( + f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys" + ) + if len(missing) > 0: + print(f"Missing Keys: {missing}") + if len(unexpected) > 0: + print(f"Unexpected Keys: {unexpected}") + + @torch.no_grad() + def get_input( + self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False + ): + # note: restricted to non-trainable encoders currently + assert ( + not self.cond_stage_trainable + ), "trainable cond stages not yet supported for inpainting" + z, c, x, xrec, xc = super().get_input( + batch, + self.first_stage_key, + return_first_stage_outputs=True, + force_c_encode=True, + return_original_cond=True, + bs=bs, + ) + + assert exists(self.concat_keys) + c_cat = list() + for ck in self.concat_keys: + cc = ( + rearrange(batch[ck], "b h w c -> b c h w") + .to(memory_format=torch.contiguous_format) + .float() + ) + if bs is not None: + cc = cc[:bs] + cc = cc.to(self.device) + bchw = z.shape + if ck != self.masked_image_key: + cc = torch.nn.functional.interpolate(cc, size=bchw[-2:]) + else: + cc = self.get_first_stage_encoding(self.encode_first_stage(cc)) + c_cat.append(cc) + c_cat = torch.cat(c_cat, dim=1) + all_conds = {"c_concat": [c_cat], "c_crossattn": [c]} + if return_first_stage_outputs: + return z, all_conds, x, xrec, xc + return z, all_conds + + @torch.no_grad() + def log_images( + self, + batch, + N=8, + n_row=4, + sample=True, + ddim_steps=200, + ddim_eta=1.0, + return_keys=None, + quantize_denoised=True, + inpaint=True, + plot_denoise_rows=False, + plot_progressive_rows=True, + plot_diffusion_rows=True, + unconditional_guidance_scale=1.0, + unconditional_guidance_label=None, + use_ema_scope=True, + **kwargs, + ): + ema_scope = self.ema_scope if use_ema_scope else nullcontext + use_ddim = ddim_steps is not None + + log = dict() + z, c, x, xrec, xc = self.get_input( + batch, self.first_stage_key, bs=N, return_first_stage_outputs=True + ) + c_cat, c = c["c_concat"][0], c["c_crossattn"][0] + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + log["inputs"] = x + log["reconstruction"] = xrec + if self.model.conditioning_key is not None: + if hasattr(self.cond_stage_model, "decode"): + xc = self.cond_stage_model.decode(c) + log["conditioning"] = xc + elif self.cond_stage_key in ["caption", "txt"]: + xc = log_txt_as_img( + (x.shape[2], x.shape[3]), + batch[self.cond_stage_key], + size=x.shape[2] // 25, + ) + log["conditioning"] = xc + elif self.cond_stage_key == "class_label": + xc = log_txt_as_img( + (x.shape[2], x.shape[3]), + batch["human_label"], + size=x.shape[2] // 25, + ) + log["conditioning"] = xc + elif isimage(xc): + log["conditioning"] = xc + if ismap(xc): + log["original_conditioning"] = self.to_rgb(xc) + + if not (self.c_concat_log_start is None and self.c_concat_log_end is None): + log["c_concat_decoded"] = self.decode_first_stage( + c_cat[:, self.c_concat_log_start : self.c_concat_log_end] + ) + + if plot_diffusion_rows: + # get diffusion row + diffusion_row = list() + z_start = z[:n_row] + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), "1 -> b", b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(z_start) + z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) + diffusion_row.append(self.decode_first_stage(z_noisy)) + + diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W + diffusion_grid = rearrange(diffusion_row, "n b c h w -> b n c h w") + diffusion_grid = rearrange(diffusion_grid, "b n c h w -> (b n) c h w") + diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) + log["diffusion_row"] = diffusion_grid + + if sample: + # get denoise row + with ema_scope("Sampling"): + samples, z_denoise_row = self.sample_log( + cond={"c_concat": [c_cat], "c_crossattn": [c]}, + batch_size=N, + ddim=use_ddim, + ddim_steps=ddim_steps, + eta=ddim_eta, + ) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + if plot_denoise_rows: + denoise_grid = self._get_denoise_row_from_list(z_denoise_row) + log["denoise_row"] = denoise_grid + + if unconditional_guidance_scale > 1.0: + uc_cross = self.get_unconditional_conditioning( + N, unconditional_guidance_label + ) + uc_cat = c_cat + uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]} + with ema_scope("Sampling with classifier-free guidance"): + samples_cfg, _ = self.sample_log( + cond={"c_concat": [c_cat], "c_crossattn": [c]}, + batch_size=N, + ddim=use_ddim, + ddim_steps=ddim_steps, + eta=ddim_eta, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=uc_full, + ) + x_samples_cfg = self.decode_first_stage(samples_cfg) + log[ + f"samples_cfg_scale_{unconditional_guidance_scale:.2f}" + ] = x_samples_cfg + + log["masked_image"] = ( + rearrange(batch["masked_image"], "b h w c -> b c h w") + .to(memory_format=torch.contiguous_format) + .float() + ) + return log + + +class Layout2ImgDiffusion(LatentDiffusion): + # TODO: move all layout-specific hacks to this class + def __init__(self, cond_stage_key, *args, **kwargs): + assert ( + cond_stage_key == "coordinates_bbox" + ), 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"' + super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs) + + def log_images(self, batch, N=8, *args, **kwargs): + logs = super().log_images(batch=batch, N=N, *args, **kwargs) + + key = "train" if self.training else "validation" + dset = self.trainer.datamodule.datasets[key] + mapper = dset.conditional_builders[self.cond_stage_key] + + bbox_imgs = [] + map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno)) + for tknzd_bbox in batch[self.cond_stage_key][:N]: + bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256)) + bbox_imgs.append(bboximg) + + cond_img = torch.stack(bbox_imgs, dim=0) + logs["bbox_image"] = cond_img + return logs + + +class SimpleUpscaleDiffusion(LatentDiffusion): + def __init__(self, *args, low_scale_key="LR", **kwargs): + super().__init__(*args, **kwargs) + # assumes that neither the cond_stage nor the low_scale_model contain trainable params + assert not self.cond_stage_trainable + self.low_scale_key = low_scale_key + + @torch.no_grad() + def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False): + if not log_mode: + z, c = super().get_input(batch, k, force_c_encode=True, bs=bs) + else: + z, c, x, xrec, xc = super().get_input( + batch, + self.first_stage_key, + return_first_stage_outputs=True, + force_c_encode=True, + return_original_cond=True, + bs=bs, + ) + x_low = batch[self.low_scale_key][:bs] + x_low = rearrange(x_low, "b h w c -> b c h w") + x_low = x_low.to(memory_format=torch.contiguous_format).float() + + encoder_posterior = self.encode_first_stage(x_low) + zx = self.get_first_stage_encoding(encoder_posterior).detach() + all_conds = {"c_concat": [zx], "c_crossattn": [c]} + + if log_mode: + # TODO: maybe disable if too expensive + interpretability = False + if interpretability: + zx = zx[:, :, ::2, ::2] + return z, all_conds, x, xrec, xc, x_low + return z, all_conds + + @torch.no_grad() + def log_images( + self, + batch, + N=8, + n_row=4, + sample=True, + ddim_steps=200, + ddim_eta=1.0, + return_keys=None, + plot_denoise_rows=False, + plot_progressive_rows=True, + plot_diffusion_rows=True, + unconditional_guidance_scale=1.0, + unconditional_guidance_label=None, + use_ema_scope=True, + **kwargs, + ): + ema_scope = self.ema_scope if use_ema_scope else nullcontext + use_ddim = ddim_steps is not None + + log = dict() + z, c, x, xrec, xc, x_low = self.get_input( + batch, self.first_stage_key, bs=N, log_mode=True + ) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + log["inputs"] = x + log["reconstruction"] = xrec + log["x_lr"] = x_low + + if self.model.conditioning_key is not None: + if hasattr(self.cond_stage_model, "decode"): + xc = self.cond_stage_model.decode(c) + log["conditioning"] = xc + elif self.cond_stage_key in ["caption", "txt"]: + xc = log_txt_as_img( + (x.shape[2], x.shape[3]), + batch[self.cond_stage_key], + size=x.shape[2] // 25, + ) + log["conditioning"] = xc + elif self.cond_stage_key == "class_label": + xc = log_txt_as_img( + (x.shape[2], x.shape[3]), + batch["human_label"], + size=x.shape[2] // 25, + ) + log["conditioning"] = xc + elif isimage(xc): + log["conditioning"] = xc + if ismap(xc): + log["original_conditioning"] = self.to_rgb(xc) + + if sample: + # get denoise row + with ema_scope("Sampling"): + samples, z_denoise_row = self.sample_log( + cond=c, + batch_size=N, + ddim=use_ddim, + ddim_steps=ddim_steps, + eta=ddim_eta, + ) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + + if unconditional_guidance_scale > 1.0: + uc_tmp = self.get_unconditional_conditioning( + N, unconditional_guidance_label + ) + uc = dict() + for k in c: + if k == "c_crossattn": + assert isinstance(c[k], list) and len(c[k]) == 1 + uc[k] = [uc_tmp] + elif isinstance(c[k], list): + uc[k] = [c[k][i] for i in range(len(c[k]))] + else: + uc[k] = c[k] + + with ema_scope("Sampling with classifier-free guidance"): + samples_cfg, _ = self.sample_log( + cond=c, + batch_size=N, + ddim=use_ddim, + ddim_steps=ddim_steps, + eta=ddim_eta, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=uc, + ) + x_samples_cfg = self.decode_first_stage(samples_cfg) + log[ + f"samples_cfg_scale_{unconditional_guidance_scale:.2f}" + ] = x_samples_cfg + return log + + +class MultiCatFrameDiffusion(LatentDiffusion): + def __init__(self, *args, low_scale_key="LR", **kwargs): + super().__init__(*args, **kwargs) + # assumes that neither the cond_stage nor the low_scale_model contain trainable params + assert not self.cond_stage_trainable + self.low_scale_key = low_scale_key + + @torch.no_grad() + def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False): + n = 2 + if not log_mode: + z, c = super().get_input(batch, k, force_c_encode=True, bs=bs) + else: + z, c, x, xrec, xc = super().get_input( + batch, + self.first_stage_key, + return_first_stage_outputs=True, + force_c_encode=True, + return_original_cond=True, + bs=bs, + ) + cat_conds = batch[self.low_scale_key][:bs] + cats = [] + for i in range(n): + x_low = cat_conds[:, :, :, 3 * i : 3 * (i + 1)] + x_low = rearrange(x_low, "b h w c -> b c h w") + x_low = x_low.to(memory_format=torch.contiguous_format).float() + encoder_posterior = self.encode_first_stage(x_low) + zx = self.get_first_stage_encoding(encoder_posterior).detach() + cats.append(zx) + + all_conds = {"c_concat": [torch.cat(cats, dim=1)], "c_crossattn": [c]} + + if log_mode: + # TODO: maybe disable if too expensive + interpretability = False + if interpretability: + zx = zx[:, :, ::2, ::2] + return z, all_conds, x, xrec, xc, x_low + return z, all_conds + + @torch.no_grad() + def log_images( + self, + batch, + N=8, + n_row=4, + sample=True, + ddim_steps=200, + ddim_eta=1.0, + return_keys=None, + plot_denoise_rows=False, + plot_progressive_rows=True, + plot_diffusion_rows=True, + unconditional_guidance_scale=1.0, + unconditional_guidance_label=None, + use_ema_scope=True, + **kwargs, + ): + ema_scope = self.ema_scope if use_ema_scope else nullcontext + use_ddim = ddim_steps is not None + + log = dict() + z, c, x, xrec, xc, x_low = self.get_input( + batch, self.first_stage_key, bs=N, log_mode=True + ) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + log["inputs"] = x + log["reconstruction"] = xrec + log["x_lr"] = x_low + + if self.model.conditioning_key is not None: + if hasattr(self.cond_stage_model, "decode"): + xc = self.cond_stage_model.decode(c) + log["conditioning"] = xc + elif self.cond_stage_key in ["caption", "txt"]: + xc = log_txt_as_img( + (x.shape[2], x.shape[3]), + batch[self.cond_stage_key], + size=x.shape[2] // 25, + ) + log["conditioning"] = xc + elif self.cond_stage_key == "class_label": + xc = log_txt_as_img( + (x.shape[2], x.shape[3]), + batch["human_label"], + size=x.shape[2] // 25, + ) + log["conditioning"] = xc + elif isimage(xc): + log["conditioning"] = xc + if ismap(xc): + log["original_conditioning"] = self.to_rgb(xc) + + if sample: + # get denoise row + with ema_scope("Sampling"): + samples, z_denoise_row = self.sample_log( + cond=c, + batch_size=N, + ddim=use_ddim, + ddim_steps=ddim_steps, + eta=ddim_eta, + ) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + + if unconditional_guidance_scale > 1.0: + uc_tmp = self.get_unconditional_conditioning( + N, unconditional_guidance_label + ) + uc = dict() + for k in c: + if k == "c_crossattn": + assert isinstance(c[k], list) and len(c[k]) == 1 + uc[k] = [uc_tmp] + elif isinstance(c[k], list): + uc[k] = [c[k][i] for i in range(len(c[k]))] + else: + uc[k] = c[k] + + with ema_scope("Sampling with classifier-free guidance"): + samples_cfg, _ = self.sample_log( + cond=c, + batch_size=N, + ddim=use_ddim, + ddim_steps=ddim_steps, + eta=ddim_eta, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=uc, + ) + x_samples_cfg = self.decode_first_stage(samples_cfg) + log[ + f"samples_cfg_scale_{unconditional_guidance_scale:.2f}" + ] = x_samples_cfg + return log diff --git a/extern/ldm_zero123/models/diffusion/plms.py b/extern/ldm_zero123/models/diffusion/plms.py new file mode 100755 index 0000000..4e61886 --- /dev/null +++ b/extern/ldm_zero123/models/diffusion/plms.py @@ -0,0 +1,383 @@ +"""SAMPLING ONLY.""" + +from functools import partial + +import numpy as np +import torch +from tqdm import tqdm + +from extern.ldm_zero123.models.diffusion.sampling_util import norm_thresholding +from extern.ldm_zero123.modules.diffusionmodules.util import ( + make_ddim_sampling_parameters, + make_ddim_timesteps, + noise_like, +) + + +class PLMSSampler(object): + def __init__(self, model, schedule="linear", **kwargs): + super().__init__() + self.model = model + self.ddpm_num_timesteps = model.num_timesteps + self.schedule = schedule + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != torch.device("cuda"): + attr = attr.to(torch.device("cuda")) + setattr(self, name, attr) + + def make_schedule( + self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True + ): + if ddim_eta != 0: + raise ValueError("ddim_eta must be 0 for PLMS") + self.ddim_timesteps = make_ddim_timesteps( + ddim_discr_method=ddim_discretize, + num_ddim_timesteps=ddim_num_steps, + num_ddpm_timesteps=self.ddpm_num_timesteps, + verbose=verbose, + ) + alphas_cumprod = self.model.alphas_cumprod + assert ( + alphas_cumprod.shape[0] == self.ddpm_num_timesteps + ), "alphas have to be defined for each timestep" + to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) + + self.register_buffer("betas", to_torch(self.model.betas)) + self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod)) + self.register_buffer( + "alphas_cumprod_prev", to_torch(self.model.alphas_cumprod_prev) + ) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer( + "sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod.cpu())) + ) + self.register_buffer( + "sqrt_one_minus_alphas_cumprod", + to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())), + ) + self.register_buffer( + "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod.cpu())) + ) + self.register_buffer( + "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu())) + ) + self.register_buffer( + "sqrt_recipm1_alphas_cumprod", + to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)), + ) + + # ddim sampling parameters + ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters( + alphacums=alphas_cumprod.cpu(), + ddim_timesteps=self.ddim_timesteps, + eta=ddim_eta, + verbose=verbose, + ) + self.register_buffer("ddim_sigmas", ddim_sigmas) + self.register_buffer("ddim_alphas", ddim_alphas) + self.register_buffer("ddim_alphas_prev", ddim_alphas_prev) + self.register_buffer("ddim_sqrt_one_minus_alphas", np.sqrt(1.0 - ddim_alphas)) + sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( + (1 - self.alphas_cumprod_prev) + / (1 - self.alphas_cumprod) + * (1 - self.alphas_cumprod / self.alphas_cumprod_prev) + ) + self.register_buffer( + "ddim_sigmas_for_original_num_steps", sigmas_for_original_sampling_steps + ) + + @torch.no_grad() + def sample( + self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0.0, + mask=None, + x0=None, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + dynamic_threshold=None, + **kwargs, + ): + if conditioning is not None: + if isinstance(conditioning, dict): + ctmp = conditioning[list(conditioning.keys())[0]] + while isinstance(ctmp, list): + ctmp = ctmp[0] + cbs = ctmp.shape[0] + if cbs != batch_size: + print( + f"Warning: Got {cbs} conditionings but batch-size is {batch_size}" + ) + else: + if conditioning.shape[0] != batch_size: + print( + f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}" + ) + + self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + print(f"Data shape for PLMS sampling is {size}") + + samples, intermediates = self.plms_sampling( + conditioning, + size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, + x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + dynamic_threshold=dynamic_threshold, + ) + return samples, intermediates + + @torch.no_grad() + def plms_sampling( + self, + cond, + shape, + x_T=None, + ddim_use_original_steps=False, + callback=None, + timesteps=None, + quantize_denoised=False, + mask=None, + x0=None, + img_callback=None, + log_every_t=100, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + dynamic_threshold=None, + ): + device = self.model.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + if timesteps is None: + timesteps = ( + self.ddpm_num_timesteps + if ddim_use_original_steps + else self.ddim_timesteps + ) + elif timesteps is not None and not ddim_use_original_steps: + subset_end = ( + int( + min(timesteps / self.ddim_timesteps.shape[0], 1) + * self.ddim_timesteps.shape[0] + ) + - 1 + ) + timesteps = self.ddim_timesteps[:subset_end] + + intermediates = {"x_inter": [img], "pred_x0": [img]} + time_range = ( + list(reversed(range(0, timesteps))) + if ddim_use_original_steps + else np.flip(timesteps) + ) + total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] + print(f"Running PLMS Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc="PLMS Sampler", total=total_steps) + old_eps = [] + + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((b,), step, device=device, dtype=torch.long) + ts_next = torch.full( + (b,), + time_range[min(i + 1, len(time_range) - 1)], + device=device, + dtype=torch.long, + ) + + if mask is not None: + assert x0 is not None + img_orig = self.model.q_sample( + x0, ts + ) # TODO: deterministic forward pass? + img = img_orig * mask + (1.0 - mask) * img + + outs = self.p_sample_plms( + img, + cond, + ts, + index=index, + use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, + temperature=temperature, + noise_dropout=noise_dropout, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + old_eps=old_eps, + t_next=ts_next, + dynamic_threshold=dynamic_threshold, + ) + img, pred_x0, e_t = outs + old_eps.append(e_t) + if len(old_eps) >= 4: + old_eps.pop(0) + if callback: + callback(i) + if img_callback: + img_callback(pred_x0, i) + + if index % log_every_t == 0 or index == total_steps - 1: + intermediates["x_inter"].append(img) + intermediates["pred_x0"].append(pred_x0) + + return img, intermediates + + @torch.no_grad() + def p_sample_plms( + self, + x, + c, + t, + index, + repeat_noise=False, + use_original_steps=False, + quantize_denoised=False, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + old_eps=None, + t_next=None, + dynamic_threshold=None, + ): + b, *_, device = *x.shape, x.device + + def get_model_output(x, t): + if ( + unconditional_conditioning is None + or unconditional_guidance_scale == 1.0 + ): + e_t = self.model.apply_model(x, t, c) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t] * 2) + if isinstance(c, dict): + assert isinstance(unconditional_conditioning, dict) + c_in = dict() + for k in c: + if isinstance(c[k], list): + c_in[k] = [ + torch.cat([unconditional_conditioning[k][i], c[k][i]]) + for i in range(len(c[k])) + ] + else: + c_in[k] = torch.cat([unconditional_conditioning[k], c[k]]) + else: + c_in = torch.cat([unconditional_conditioning, c]) + e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) + e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) + + if score_corrector is not None: + assert self.model.parameterization == "eps" + e_t = score_corrector.modify_score( + self.model, e_t, x, t, c, **corrector_kwargs + ) + + return e_t + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = ( + self.model.alphas_cumprod_prev + if use_original_steps + else self.ddim_alphas_prev + ) + sqrt_one_minus_alphas = ( + self.model.sqrt_one_minus_alphas_cumprod + if use_original_steps + else self.ddim_sqrt_one_minus_alphas + ) + sigmas = ( + self.model.ddim_sigmas_for_original_num_steps + if use_original_steps + else self.ddim_sigmas + ) + + def get_x_prev_and_pred_x0(e_t, index): + # select parameters corresponding to the currently considered timestep + a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) + a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) + sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) + sqrt_one_minus_at = torch.full( + (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device + ) + + # current prediction for x_0 + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + if dynamic_threshold is not None: + pred_x0 = norm_thresholding(pred_x0, dynamic_threshold) + # direction pointing to x_t + dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.0: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev, pred_x0 + + e_t = get_model_output(x, t) + if len(old_eps) == 0: + # Pseudo Improved Euler (2nd order) + x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) + e_t_next = get_model_output(x_prev, t_next) + e_t_prime = (e_t + e_t_next) / 2 + elif len(old_eps) == 1: + # 2nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (3 * e_t - old_eps[-1]) / 2 + elif len(old_eps) == 2: + # 3nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 + elif len(old_eps) >= 3: + # 4nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = ( + 55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3] + ) / 24 + + x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) + + return x_prev, pred_x0, e_t diff --git a/extern/ldm_zero123/models/diffusion/sampling_util.py b/extern/ldm_zero123/models/diffusion/sampling_util.py new file mode 100755 index 0000000..1d0df15 --- /dev/null +++ b/extern/ldm_zero123/models/diffusion/sampling_util.py @@ -0,0 +1,51 @@ +import numpy as np +import torch + + +def append_dims(x, target_dims): + """Appends dimensions to the end of a tensor until it has target_dims dimensions. + From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py""" + dims_to_append = target_dims - x.ndim + if dims_to_append < 0: + raise ValueError( + f"input has {x.ndim} dims but target_dims is {target_dims}, which is less" + ) + return x[(...,) + (None,) * dims_to_append] + + +def renorm_thresholding(x0, value): + # renorm + pred_max = x0.max() + pred_min = x0.min() + pred_x0 = (x0 - pred_min) / (pred_max - pred_min) # 0 ... 1 + pred_x0 = 2 * pred_x0 - 1.0 # -1 ... 1 + + s = torch.quantile(rearrange(pred_x0, "b ... -> b (...)").abs(), value, dim=-1) + s.clamp_(min=1.0) + s = s.view(-1, *((1,) * (pred_x0.ndim - 1))) + + # clip by threshold + # pred_x0 = pred_x0.clamp(-s, s) / s # needs newer pytorch # TODO bring back to pure-gpu with min/max + + # temporary hack: numpy on cpu + pred_x0 = ( + np.clip(pred_x0.cpu().numpy(), -s.cpu().numpy(), s.cpu().numpy()) + / s.cpu().numpy() + ) + pred_x0 = torch.tensor(pred_x0).to(self.model.device) + + # re.renorm + pred_x0 = (pred_x0 + 1.0) / 2.0 # 0 ... 1 + pred_x0 = (pred_max - pred_min) * pred_x0 + pred_min # orig range + return pred_x0 + + +def norm_thresholding(x0, value): + s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim) + return x0 * (value / s) + + +def spatial_norm_thresholding(x0, value): + # b c h w + s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value) + return x0 * (value / s) diff --git a/extern/ldm_zero123/modules/attention.py b/extern/ldm_zero123/modules/attention.py new file mode 100755 index 0000000..e2a1c9e --- /dev/null +++ b/extern/ldm_zero123/modules/attention.py @@ -0,0 +1,301 @@ +import math +from inspect import isfunction + +import torch +import torch.nn.functional as F +from einops import rearrange, repeat +from torch import einsum, nn + +from extern.ldm_zero123.modules.diffusionmodules.util import checkpoint + + +def exists(val): + return val is not None + + +def uniq(arr): + return {el: True for el in arr}.keys() + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def max_neg_value(t): + return -torch.finfo(t.dtype).max + + +def init_(tensor): + dim = tensor.shape[-1] + std = 1 / math.sqrt(dim) + tensor.uniform_(-std, std) + return tensor + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = ( + nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU()) + if not glu + else GEGLU(dim, inner_dim) + ) + + self.net = nn.Sequential( + project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def Normalize(in_channels): + return torch.nn.GroupNorm( + num_groups=32, num_channels=in_channels, eps=1e-6, affine=True + ) + + +class LinearAttention(nn.Module): + def __init__(self, dim, heads=4, dim_head=32): + super().__init__() + self.heads = heads + hidden_dim = dim_head * heads + self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False) + self.to_out = nn.Conv2d(hidden_dim, dim, 1) + + def forward(self, x): + b, c, h, w = x.shape + qkv = self.to_qkv(x) + q, k, v = rearrange( + qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3 + ) + k = k.softmax(dim=-1) + context = torch.einsum("bhdn,bhen->bhde", k, v) + out = torch.einsum("bhde,bhdn->bhen", context, q) + out = rearrange( + out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w + ) + return self.to_out(out) + + +class SpatialSelfAttention(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = rearrange(q, "b c h w -> b (h w) c") + k = rearrange(k, "b c h w -> b c (h w)") + w_ = torch.einsum("bij,bjk->bik", q, k) + + w_ = w_ * (int(c) ** (-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = rearrange(v, "b c h w -> b c (h w)") + w_ = rearrange(w_, "b i j -> b j i") + h_ = torch.einsum("bij,bjk->bik", v, w_) + h_ = rearrange(h_, "b c (h w) -> b c h w", h=h) + h_ = self.proj_out(h_) + + return x + h_ + + +class CrossAttention(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.scale = dim_head**-0.5 + self.heads = heads + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, query_dim), nn.Dropout(dropout) + ) + + def forward(self, x, context=None, mask=None): + h = self.heads + + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + v = self.to_v(context) + + q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v)) + + sim = einsum("b i d, b j d -> b i j", q, k) * self.scale + + if exists(mask): + mask = rearrange(mask, "b ... -> b (...)") + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, "b j -> (b h) () j", h=h) + sim.masked_fill_(~mask, max_neg_value) + + # attention, what we cannot get enough of + attn = sim.softmax(dim=-1) + + out = einsum("b i j, b j d -> b i d", attn, v) + out = rearrange(out, "(b h) n d -> b n (h d)", h=h) + return self.to_out(out) + + +class BasicTransformerBlock(nn.Module): + def __init__( + self, + dim, + n_heads, + d_head, + dropout=0.0, + context_dim=None, + gated_ff=True, + checkpoint=True, + disable_self_attn=False, + ): + super().__init__() + self.disable_self_attn = disable_self_attn + self.attn1 = CrossAttention( + query_dim=dim, + heads=n_heads, + dim_head=d_head, + dropout=dropout, + context_dim=context_dim if self.disable_self_attn else None, + ) # is a self-attention if not self.disable_self_attn + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = CrossAttention( + query_dim=dim, + context_dim=context_dim, + heads=n_heads, + dim_head=d_head, + dropout=dropout, + ) # is self-attn if context is none + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + self.checkpoint = checkpoint + + def forward(self, x, context=None): + return checkpoint( + self._forward, (x, context), self.parameters(), self.checkpoint + ) + + def _forward(self, x, context=None): + x = ( + self.attn1( + self.norm1(x), context=context if self.disable_self_attn else None + ) + + x + ) + x = self.attn2(self.norm2(x), context=context) + x + x = self.ff(self.norm3(x)) + x + return x + + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + """ + + def __init__( + self, + in_channels, + n_heads, + d_head, + depth=1, + dropout=0.0, + context_dim=None, + disable_self_attn=False, + ): + super().__init__() + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = Normalize(in_channels) + + self.proj_in = nn.Conv2d( + in_channels, inner_dim, kernel_size=1, stride=1, padding=0 + ) + + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + n_heads, + d_head, + dropout=dropout, + context_dim=context_dim, + disable_self_attn=disable_self_attn, + ) + for d in range(depth) + ] + ) + + self.proj_out = zero_module( + nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) + ) + + def forward(self, x, context=None): + # note: if no context is given, cross-attention defaults to self-attention + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + x = self.proj_in(x) + x = rearrange(x, "b c h w -> b (h w) c").contiguous() + for block in self.transformer_blocks: + x = block(x, context=context) + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous() + x = self.proj_out(x) + return x + x_in diff --git a/extern/ldm_zero123/modules/diffusionmodules/__init__.py b/extern/ldm_zero123/modules/diffusionmodules/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/extern/ldm_zero123/modules/diffusionmodules/model.py b/extern/ldm_zero123/modules/diffusionmodules/model.py new file mode 100755 index 0000000..5aaefb4 --- /dev/null +++ b/extern/ldm_zero123/modules/diffusionmodules/model.py @@ -0,0 +1,1009 @@ +# pytorch_diffusion + derived encoder decoder +import math + +import numpy as np +import torch +import torch.nn as nn +from einops import rearrange + +from extern.ldm_zero123.modules.attention import LinearAttention +from extern.ldm_zero123.util import instantiate_from_config + + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) + return emb + + +def nonlinearity(x): + # swish + return x * torch.sigmoid(x) + + +def Normalize(in_channels, num_groups=32): + return torch.nn.GroupNorm( + num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True + ) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=2, padding=0 + ) + + def forward(self, x): + if self.with_conv: + pad = (0, 1, 0, 1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__( + self, + *, + in_channels, + out_channels=None, + conv_shortcut=False, + dropout, + temb_channels=512, + ): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + else: + self.nin_shortcut = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x + h + + +class LinAttnBlock(LinearAttention): + """to match AttnBlock usage""" + + def __init__(self, in_channels): + super().__init__(dim=in_channels, heads=1, dim_head=in_channels) + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = q.reshape(b, c, h * w) + q = q.permute(0, 2, 1) # b,hw,c + k = k.reshape(b, c, h * w) # b,c,hw + w_ = torch.bmm(q, k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] + w_ = w_ * (int(c) ** (-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b, c, h * w) + w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q) + h_ = torch.bmm(v, w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] + h_ = h_.reshape(b, c, h, w) + + h_ = self.proj_out(h_) + + return x + h_ + + +def make_attn(in_channels, attn_type="vanilla"): + assert attn_type in ["vanilla", "linear", "none"], f"attn_type {attn_type} unknown" + print(f"making attention of type '{attn_type}' with {in_channels} in_channels") + if attn_type == "vanilla": + return AttnBlock(in_channels) + elif attn_type == "none": + return nn.Identity(in_channels) + else: + return LinAttnBlock(in_channels) + + +class Model(nn.Module): + def __init__( + self, + *, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks, + attn_resolutions, + dropout=0.0, + resamp_with_conv=True, + in_channels, + resolution, + use_timestep=True, + use_linear_attn=False, + attn_type="vanilla", + ): + super().__init__() + if use_linear_attn: + attn_type = "linear" + self.ch = ch + self.temb_ch = self.ch * 4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList( + [ + torch.nn.Linear(self.ch, self.temb_ch), + torch.nn.Linear(self.temb_ch, self.temb_ch), + ] + ) + + # downsampling + self.conv_in = torch.nn.Conv2d( + in_channels, self.ch, kernel_size=3, stride=1, padding=1 + ) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + skip_in = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + if i_block == self.num_res_blocks: + skip_in = ch * in_ch_mult[i_level] + block.append( + ResnetBlock( + in_channels=block_in + skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, out_ch, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x, t=None, context=None): + # assert x.shape[2] == x.shape[3] == self.resolution + if context is not None: + # assume aligned context, cat along channel axis + x = torch.cat((x, context), dim=1) + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block]( + torch.cat([h, hs.pop()], dim=1), temb + ) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + def get_last_layer(self): + return self.conv_out.weight + + +class Encoder(nn.Module): + def __init__( + self, + *, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks, + attn_resolutions, + dropout=0.0, + resamp_with_conv=True, + in_channels, + resolution, + z_channels, + double_z=True, + use_linear_attn=False, + attn_type="vanilla", + **ignore_kwargs, + ): + super().__init__() + if use_linear_attn: + attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = torch.nn.Conv2d( + in_channels, self.ch, kernel_size=3, stride=1, padding=1 + ) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, + 2 * z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1, + ) + + def forward(self, x): + # timestep embedding + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__( + self, + *, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks, + attn_resolutions, + dropout=0.0, + resamp_with_conv=True, + in_channels, + resolution, + z_channels, + give_pre_end=False, + tanh_out=False, + use_linear_attn=False, + attn_type="vanilla", + **ignorekwargs, + ): + super().__init__() + if use_linear_attn: + attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.tanh_out = tanh_out + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,) + tuple(ch_mult) + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + print( + "Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape) + ) + ) + + # z to block_in + self.conv_in = torch.nn.Conv2d( + z_channels, block_in, kernel_size=3, stride=1, padding=1 + ) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, out_ch, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, z): + # assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + if self.tanh_out: + h = torch.tanh(h) + return h + + +class SimpleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, *args, **kwargs): + super().__init__() + self.model = nn.ModuleList( + [ + nn.Conv2d(in_channels, in_channels, 1), + ResnetBlock( + in_channels=in_channels, + out_channels=2 * in_channels, + temb_channels=0, + dropout=0.0, + ), + ResnetBlock( + in_channels=2 * in_channels, + out_channels=4 * in_channels, + temb_channels=0, + dropout=0.0, + ), + ResnetBlock( + in_channels=4 * in_channels, + out_channels=2 * in_channels, + temb_channels=0, + dropout=0.0, + ), + nn.Conv2d(2 * in_channels, in_channels, 1), + Upsample(in_channels, with_conv=True), + ] + ) + # end + self.norm_out = Normalize(in_channels) + self.conv_out = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x): + for i, layer in enumerate(self.model): + if i in [1, 2, 3]: + x = layer(x, None) + else: + x = layer(x) + + h = self.norm_out(x) + h = nonlinearity(h) + x = self.conv_out(h) + return x + + +class UpsampleDecoder(nn.Module): + def __init__( + self, + in_channels, + out_channels, + ch, + num_res_blocks, + resolution, + ch_mult=(2, 2), + dropout=0.0, + ): + super().__init__() + # upsampling + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + block_in = in_channels + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.res_blocks = nn.ModuleList() + self.upsample_blocks = nn.ModuleList() + for i_level in range(self.num_resolutions): + res_block = [] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + res_block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + self.res_blocks.append(nn.ModuleList(res_block)) + if i_level != self.num_resolutions - 1: + self.upsample_blocks.append(Upsample(block_in, True)) + curr_res = curr_res * 2 + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, out_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x): + # upsampling + h = x + for k, i_level in enumerate(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.res_blocks[i_level][i_block](h, None) + if i_level != self.num_resolutions - 1: + h = self.upsample_blocks[k](h) + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class LatentRescaler(nn.Module): + def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): + super().__init__() + # residual block, interpolate, residual block + self.factor = factor + self.conv_in = nn.Conv2d( + in_channels, mid_channels, kernel_size=3, stride=1, padding=1 + ) + self.res_block1 = nn.ModuleList( + [ + ResnetBlock( + in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0, + ) + for _ in range(depth) + ] + ) + self.attn = AttnBlock(mid_channels) + self.res_block2 = nn.ModuleList( + [ + ResnetBlock( + in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0, + ) + for _ in range(depth) + ] + ) + + self.conv_out = nn.Conv2d( + mid_channels, + out_channels, + kernel_size=1, + ) + + def forward(self, x): + x = self.conv_in(x) + for block in self.res_block1: + x = block(x, None) + x = torch.nn.functional.interpolate( + x, + size=( + int(round(x.shape[2] * self.factor)), + int(round(x.shape[3] * self.factor)), + ), + ) + x = self.attn(x) + for block in self.res_block2: + x = block(x, None) + x = self.conv_out(x) + return x + + +class MergedRescaleEncoder(nn.Module): + def __init__( + self, + in_channels, + ch, + resolution, + out_ch, + num_res_blocks, + attn_resolutions, + dropout=0.0, + resamp_with_conv=True, + ch_mult=(1, 2, 4, 8), + rescale_factor=1.0, + rescale_module_depth=1, + ): + super().__init__() + intermediate_chn = ch * ch_mult[-1] + self.encoder = Encoder( + in_channels=in_channels, + num_res_blocks=num_res_blocks, + ch=ch, + ch_mult=ch_mult, + z_channels=intermediate_chn, + double_z=False, + resolution=resolution, + attn_resolutions=attn_resolutions, + dropout=dropout, + resamp_with_conv=resamp_with_conv, + out_ch=None, + ) + self.rescaler = LatentRescaler( + factor=rescale_factor, + in_channels=intermediate_chn, + mid_channels=intermediate_chn, + out_channels=out_ch, + depth=rescale_module_depth, + ) + + def forward(self, x): + x = self.encoder(x) + x = self.rescaler(x) + return x + + +class MergedRescaleDecoder(nn.Module): + def __init__( + self, + z_channels, + out_ch, + resolution, + num_res_blocks, + attn_resolutions, + ch, + ch_mult=(1, 2, 4, 8), + dropout=0.0, + resamp_with_conv=True, + rescale_factor=1.0, + rescale_module_depth=1, + ): + super().__init__() + tmp_chn = z_channels * ch_mult[-1] + self.decoder = Decoder( + out_ch=out_ch, + z_channels=tmp_chn, + attn_resolutions=attn_resolutions, + dropout=dropout, + resamp_with_conv=resamp_with_conv, + in_channels=None, + num_res_blocks=num_res_blocks, + ch_mult=ch_mult, + resolution=resolution, + ch=ch, + ) + self.rescaler = LatentRescaler( + factor=rescale_factor, + in_channels=z_channels, + mid_channels=tmp_chn, + out_channels=tmp_chn, + depth=rescale_module_depth, + ) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Upsampler(nn.Module): + def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): + super().__init__() + assert out_size >= in_size + num_blocks = int(np.log2(out_size // in_size)) + 1 + factor_up = 1.0 + (out_size % in_size) + print( + f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}" + ) + self.rescaler = LatentRescaler( + factor=factor_up, + in_channels=in_channels, + mid_channels=2 * in_channels, + out_channels=in_channels, + ) + self.decoder = Decoder( + out_ch=out_channels, + resolution=out_size, + z_channels=in_channels, + num_res_blocks=2, + attn_resolutions=[], + in_channels=None, + ch=in_channels, + ch_mult=[ch_mult for _ in range(num_blocks)], + ) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Resize(nn.Module): + def __init__(self, in_channels=None, learned=False, mode="bilinear"): + super().__init__() + self.with_conv = learned + self.mode = mode + if self.with_conv: + print( + f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode" + ) + raise NotImplementedError() + assert in_channels is not None + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=4, stride=2, padding=1 + ) + + def forward(self, x, scale_factor=1.0): + if scale_factor == 1.0: + return x + else: + x = torch.nn.functional.interpolate( + x, mode=self.mode, align_corners=False, scale_factor=scale_factor + ) + return x + + +class FirstStagePostProcessor(nn.Module): + def __init__( + self, + ch_mult: list, + in_channels, + pretrained_model: nn.Module = None, + reshape=False, + n_channels=None, + dropout=0.0, + pretrained_config=None, + ): + super().__init__() + if pretrained_config is None: + assert ( + pretrained_model is not None + ), 'Either "pretrained_model" or "pretrained_config" must not be None' + self.pretrained_model = pretrained_model + else: + assert ( + pretrained_config is not None + ), 'Either "pretrained_model" or "pretrained_config" must not be None' + self.instantiate_pretrained(pretrained_config) + + self.do_reshape = reshape + + if n_channels is None: + n_channels = self.pretrained_model.encoder.ch + + self.proj_norm = Normalize(in_channels, num_groups=in_channels // 2) + self.proj = nn.Conv2d( + in_channels, n_channels, kernel_size=3, stride=1, padding=1 + ) + + blocks = [] + downs = [] + ch_in = n_channels + for m in ch_mult: + blocks.append( + ResnetBlock( + in_channels=ch_in, out_channels=m * n_channels, dropout=dropout + ) + ) + ch_in = m * n_channels + downs.append(Downsample(ch_in, with_conv=False)) + + self.model = nn.ModuleList(blocks) + self.downsampler = nn.ModuleList(downs) + + def instantiate_pretrained(self, config): + model = instantiate_from_config(config) + self.pretrained_model = model.eval() + # self.pretrained_model.train = False + for param in self.pretrained_model.parameters(): + param.requires_grad = False + + @torch.no_grad() + def encode_with_pretrained(self, x): + c = self.pretrained_model.encode(x) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + return c + + def forward(self, x): + z_fs = self.encode_with_pretrained(x) + z = self.proj_norm(z_fs) + z = self.proj(z) + z = nonlinearity(z) + + for submodel, downmodel in zip(self.model, self.downsampler): + z = submodel(z, temb=None) + z = downmodel(z) + + if self.do_reshape: + z = rearrange(z, "b c h w -> b (h w) c") + return z diff --git a/extern/ldm_zero123/modules/diffusionmodules/openaimodel.py b/extern/ldm_zero123/modules/diffusionmodules/openaimodel.py new file mode 100755 index 0000000..ed9c1cd --- /dev/null +++ b/extern/ldm_zero123/modules/diffusionmodules/openaimodel.py @@ -0,0 +1,1060 @@ +import math +from abc import abstractmethod +from functools import partial +from typing import Iterable + +import numpy as np +import torch as th +import torch.nn as nn +import torch.nn.functional as F + +from extern.ldm_zero123.modules.attention import SpatialTransformer +from extern.ldm_zero123.modules.diffusionmodules.util import ( + avg_pool_nd, + checkpoint, + conv_nd, + linear, + normalization, + timestep_embedding, + zero_module, +) +from extern.ldm_zero123.util import exists + + +# dummy replace +def convert_module_to_f16(x): + pass + + +def convert_module_to_f32(x): + pass + + +## go +class AttentionPool2d(nn.Module): + """ + Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py + """ + + def __init__( + self, + spacial_dim: int, + embed_dim: int, + num_heads_channels: int, + output_dim: int = None, + ): + super().__init__() + self.positional_embedding = nn.Parameter( + th.randn(embed_dim, spacial_dim**2 + 1) / embed_dim**0.5 + ) + self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1) + self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1) + self.num_heads = embed_dim // num_heads_channels + self.attention = QKVAttention(self.num_heads) + + def forward(self, x): + b, c, *_spatial = x.shape + x = x.reshape(b, c, -1) # NC(HW) + x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1) + x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1) + x = self.qkv_proj(x) + x = self.attention(x) + x = self.c_proj(x) + return x[:, :, 0] + + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + + +class TimestepEmbedSequential(nn.Sequential, TimestepBlock): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, x, emb, context=None): + for layer in self: + if isinstance(layer, TimestepBlock): + x = layer(x, emb) + elif isinstance(layer, SpatialTransformer): + x = layer(x, context) + else: + x = layer(x) + return x + + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = conv_nd( + dims, self.channels, self.out_channels, 3, padding=padding + ) + + def forward(self, x): + assert x.shape[1] == self.channels + if self.dims == 3: + x = F.interpolate( + x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" + ) + else: + x = F.interpolate(x, scale_factor=2, mode="nearest") + if self.use_conv: + x = self.conv(x) + return x + + +class TransposedUpsample(nn.Module): + "Learned 2x upsampling without padding" + + def __init__(self, channels, out_channels=None, ks=5): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + + self.up = nn.ConvTranspose2d( + self.channels, self.out_channels, kernel_size=ks, stride=2 + ) + + def forward(self, x): + return self.up(x) + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd( + dims, + self.channels, + self.out_channels, + 3, + stride=stride, + padding=padding, + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class ResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param use_checkpoint: if True, use gradient checkpointing on this module. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + dims=2, + use_checkpoint=False, + up=False, + down=False, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + conv_nd(dims, channels, self.out_channels, 3, padding=1), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims) + self.x_upd = Upsample(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.emb_layers = nn.Sequential( + nn.SiLU(), + linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, + ), + ) + self.out_layers = nn.Sequential( + normalization(self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module( + conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = conv_nd( + dims, channels, self.out_channels, 3, padding=1 + ) + else: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) + + def forward(self, x, emb): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + return checkpoint( + self._forward, (x, emb), self.parameters(), self.use_checkpoint + ) + + def _forward(self, x, emb): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = th.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class AttentionBlock(nn.Module): + """ + An attention block that allows spatial positions to attend to each other. + Originally ported from here, but adapted to the N-d case. + https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. + """ + + def __init__( + self, + channels, + num_heads=1, + num_head_channels=-1, + use_checkpoint=False, + use_new_attention_order=False, + ): + super().__init__() + self.channels = channels + if num_head_channels == -1: + self.num_heads = num_heads + else: + assert ( + channels % num_head_channels == 0 + ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" + self.num_heads = channels // num_head_channels + self.use_checkpoint = use_checkpoint + self.norm = normalization(channels) + self.qkv = conv_nd(1, channels, channels * 3, 1) + if use_new_attention_order: + # split qkv before split heads + self.attention = QKVAttention(self.num_heads) + else: + # split heads before split qkv + self.attention = QKVAttentionLegacy(self.num_heads) + + self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) + + def forward(self, x): + return checkpoint( + self._forward, (x,), self.parameters(), True + ) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!! + # return pt_checkpoint(self._forward, x) # pytorch + + def _forward(self, x): + b, c, *spatial = x.shape + x = x.reshape(b, c, -1) + qkv = self.qkv(self.norm(x)) + h = self.attention(qkv) + h = self.proj_out(h) + return (x + h).reshape(b, c, *spatial) + + +def count_flops_attn(model, _x, y): + """ + A counter for the `thop` package to count the operations in an + attention operation. + Meant to be used like: + macs, params = thop.profile( + model, + inputs=(inputs, timestamps), + custom_ops={QKVAttention: QKVAttention.count_flops}, + ) + """ + b, c, *spatial = y[0].shape + num_spatial = int(np.prod(spatial)) + # We perform two matmuls with the same number of ops. + # The first computes the weight matrix, the second computes + # the combination of the value vectors. + matmul_ops = 2 * b * (num_spatial**2) * c + model.total_ops += th.DoubleTensor([matmul_ops]) + + +class QKVAttentionLegacy(nn.Module): + """ + A module which performs QKV attention. Matches legacy QKVAttention + input/output heads shaping + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = th.einsum( + "bct,bcs->bts", q * scale, k * scale + ) # More stable with f16 than dividing afterwards + weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) + a = th.einsum("bts,bcs->bct", weight, v) + return a.reshape(bs, -1, length) + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class QKVAttention(nn.Module): + """ + A module which performs QKV attention and splits in a different order. + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.chunk(3, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = th.einsum( + "bct,bcs->bts", + (q * scale).view(bs * self.n_heads, ch, length), + (k * scale).view(bs * self.n_heads, ch, length), + ) # More stable with f16 than dividing afterwards + weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) + a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) + return a.reshape(bs, -1, length) + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class UNetModel(nn.Module): + """ + The full UNet model with attention and timestep embedding. + :param in_channels: channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param use_checkpoint: use gradient checkpointing to reduce memory usage. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + use_checkpoint=False, + use_fp16=False, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + disable_self_attentions=None, + num_attention_blocks=None, + ): + super().__init__() + if use_spatial_transformer: + assert ( + context_dim is not None + ), "Fool!! You forgot to include the dimension of your cross-attention conditioning..." + + if context_dim is not None: + assert ( + use_spatial_transformer + ), "Fool!! You forgot to use the spatial transformer for your cross-attention conditioning..." + from omegaconf.listconfig import ListConfig + + if type(context_dim) == ListConfig: + context_dim = list(context_dim) + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert ( + num_head_channels != -1 + ), "Either num_heads or num_head_channels has to be set" + + if num_head_channels == -1: + assert ( + num_heads != -1 + ), "Either num_heads or num_head_channels has to be set" + + self.image_size = image_size + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + if isinstance(num_res_blocks, int): + self.num_res_blocks = len(channel_mult) * [num_res_blocks] + else: + if len(num_res_blocks) != len(channel_mult): + raise ValueError( + "provide num_res_blocks either as an int (globally constant) or " + "as a list/tuple (per-level) with the same length as channel_mult" + ) + self.num_res_blocks = num_res_blocks + # self.num_res_blocks = num_res_blocks + if disable_self_attentions is not None: + # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not + assert len(disable_self_attentions) == len(channel_mult) + if num_attention_blocks is not None: + assert len(num_attention_blocks) == len(self.num_res_blocks) + assert all( + map( + lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], + range(len(num_attention_blocks)), + ) + ) + print( + f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. " + f"This option has LESS priority than attention_resolutions {attention_resolutions}, " + f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, " + f"attention will still not be set." + ) # todo: convert to warning + + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.dtype = th.float16 if use_fp16 else th.float32 + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.predict_codebook_ids = n_embed is not None + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + linear(model_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + + if self.num_classes is not None: + self.label_emb = nn.Embedding(num_classes, time_embed_dim) + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + conv_nd(dims, in_channels, model_channels, 3, padding=1) + ) + ] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for nr in range(self.num_res_blocks[level]): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + # num_heads = 1 + dim_head = ( + ch // num_heads + if use_spatial_transformer + else num_head_channels + ) + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if ( + not exists(num_attention_blocks) + or nr < num_attention_blocks[level] + ): + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) + if not use_spatial_transformer + else SpatialTransformer( + ch, + num_heads, + dim_head, + depth=transformer_depth, + context_dim=context_dim, + disable_self_attn=disabled_sa, + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + # num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) + if not use_spatial_transformer + else SpatialTransformer( # always uses a self-attn + ch, + num_heads, + dim_head, + depth=transformer_depth, + context_dim=context_dim, + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + ) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(self.num_res_blocks[level] + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock( + ch + ich, + time_embed_dim, + dropout, + out_channels=model_channels * mult, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = model_channels * mult + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + # num_heads = 1 + dim_head = ( + ch // num_heads + if use_spatial_transformer + else num_head_channels + ) + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if ( + not exists(num_attention_blocks) + or i < num_attention_blocks[level] + ): + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads_upsample, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) + if not use_spatial_transformer + else SpatialTransformer( + ch, + num_heads, + dim_head, + depth=transformer_depth, + context_dim=context_dim, + disable_self_attn=disabled_sa, + ) + ) + if level and i == self.num_res_blocks[level]: + out_ch = ch + layers.append( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + up=True, + ) + if resblock_updown + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), + ) + if self.predict_codebook_ids: + self.id_predictor = nn.Sequential( + normalization(ch), + conv_nd(dims, model_channels, n_embed, 1), + # nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits + ) + + def convert_to_fp16(self): + """ + Convert the torso of the model to float16. + """ + self.input_blocks.apply(convert_module_to_f16) + self.middle_block.apply(convert_module_to_f16) + self.output_blocks.apply(convert_module_to_f16) + + def convert_to_fp32(self): + """ + Convert the torso of the model to float32. + """ + self.input_blocks.apply(convert_module_to_f32) + self.middle_block.apply(convert_module_to_f32) + self.output_blocks.apply(convert_module_to_f32) + + def forward(self, x, timesteps=None, context=None, y=None, **kwargs): + """ + Apply the model to an input batch. + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps. + :param context: conditioning plugged in via crossattn + :param y: an [N] Tensor of labels, if class-conditional. + :return: an [N x C x ...] Tensor of outputs. + """ + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + hs = [] + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype) + emb = self.time_embed(t_emb) + + if self.num_classes is not None: + assert y.shape == (x.shape[0],) + emb = emb + self.label_emb(y) + + h = x.type(self.dtype) + for module in self.input_blocks: + h = module(h, emb, context) + hs.append(h) + h = self.middle_block(h, emb, context) + for module in self.output_blocks: + h = th.cat([h, hs.pop()], dim=1) + h = module(h, emb, context) + h = h.type(x.dtype) + if self.predict_codebook_ids: + return self.id_predictor(h) + else: + return self.out(h) + + +class EncoderUNetModel(nn.Module): + """ + The half UNet model with attention and timestep embedding. + For usage, see UNet. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + use_checkpoint=False, + use_fp16=False, + num_heads=1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + pool="adaptive", + *args, + **kwargs, + ): + super().__init__() + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + self.num_res_blocks = num_res_blocks + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.use_checkpoint = use_checkpoint + self.dtype = th.float16 if use_fp16 else th.float32 + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + linear(model_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + conv_nd(dims, in_channels, model_channels, 3, padding=1) + ) + ] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for _ in range(num_res_blocks): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=num_head_channels, + use_new_attention_order=use_new_attention_order, + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=num_head_channels, + use_new_attention_order=use_new_attention_order, + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + ) + self._feature_size += ch + self.pool = pool + if pool == "adaptive": + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + nn.AdaptiveAvgPool2d((1, 1)), + zero_module(conv_nd(dims, ch, out_channels, 1)), + nn.Flatten(), + ) + elif pool == "attention": + assert num_head_channels != -1 + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + AttentionPool2d( + (image_size // ds), ch, num_head_channels, out_channels + ), + ) + elif pool == "spatial": + self.out = nn.Sequential( + nn.Linear(self._feature_size, 2048), + nn.ReLU(), + nn.Linear(2048, self.out_channels), + ) + elif pool == "spatial_v2": + self.out = nn.Sequential( + nn.Linear(self._feature_size, 2048), + normalization(2048), + nn.SiLU(), + nn.Linear(2048, self.out_channels), + ) + else: + raise NotImplementedError(f"Unexpected {pool} pooling") + + def convert_to_fp16(self): + """ + Convert the torso of the model to float16. + """ + self.input_blocks.apply(convert_module_to_f16) + self.middle_block.apply(convert_module_to_f16) + + def convert_to_fp32(self): + """ + Convert the torso of the model to float32. + """ + self.input_blocks.apply(convert_module_to_f32) + self.middle_block.apply(convert_module_to_f32) + + def forward(self, x, timesteps): + """ + Apply the model to an input batch. + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps. + :return: an [N x K] Tensor of outputs. + """ + emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + + results = [] + h = x.type(self.dtype) + for module in self.input_blocks: + h = module(h, emb) + if self.pool.startswith("spatial"): + results.append(h.type(x.dtype).mean(dim=(2, 3))) + h = self.middle_block(h, emb) + if self.pool.startswith("spatial"): + results.append(h.type(x.dtype).mean(dim=(2, 3))) + h = th.cat(results, axis=-1) + return self.out(h) + else: + h = h.type(x.dtype) + return self.out(h) diff --git a/extern/ldm_zero123/modules/diffusionmodules/util.py b/extern/ldm_zero123/modules/diffusionmodules/util.py new file mode 100755 index 0000000..c707b65 --- /dev/null +++ b/extern/ldm_zero123/modules/diffusionmodules/util.py @@ -0,0 +1,297 @@ +# adopted from +# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py +# and +# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +# and +# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py +# +# thanks! + + +import math +import os + +import numpy as np +import torch +import torch.nn as nn +from einops import repeat + +from extern.ldm_zero123.util import instantiate_from_config + + +def make_beta_schedule( + schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3 +): + if schedule == "linear": + betas = ( + torch.linspace( + linear_start**0.5, linear_end**0.5, n_timestep, dtype=torch.float64 + ) + ** 2 + ) + + elif schedule == "cosine": + timesteps = ( + torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s + ) + alphas = timesteps / (1 + cosine_s) * np.pi / 2 + alphas = torch.cos(alphas).pow(2) + alphas = alphas / alphas[0] + betas = 1 - alphas[1:] / alphas[:-1] + betas = np.clip(betas, a_min=0, a_max=0.999) + + elif schedule == "sqrt_linear": + betas = torch.linspace( + linear_start, linear_end, n_timestep, dtype=torch.float64 + ) + elif schedule == "sqrt": + betas = ( + torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) + ** 0.5 + ) + else: + raise ValueError(f"schedule '{schedule}' unknown.") + return betas.numpy() + + +def make_ddim_timesteps( + ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True +): + if ddim_discr_method == "uniform": + c = num_ddpm_timesteps // num_ddim_timesteps + ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) + elif ddim_discr_method == "quad": + ddim_timesteps = ( + (np.linspace(0, np.sqrt(num_ddpm_timesteps * 0.8), num_ddim_timesteps)) ** 2 + ).astype(int) + else: + raise NotImplementedError( + f'There is no ddim discretization method called "{ddim_discr_method}"' + ) + + # assert ddim_timesteps.shape[0] == num_ddim_timesteps + # add one to get the final alpha values right (the ones from first scale to data during sampling) + steps_out = ddim_timesteps + 1 + if verbose: + print(f"Selected timesteps for ddim sampler: {steps_out}") + return steps_out + + +def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): + # select alphas for computing the variance schedule + alphas = alphacums[ddim_timesteps] + alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) + + # according the the formula provided in https://arxiv.org/abs/2010.02502 + sigmas = eta * np.sqrt( + (1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev) + ) + if verbose: + print( + f"Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}" + ) + print( + f"For the chosen value of eta, which is {eta}, " + f"this results in the following sigma_t schedule for ddim sampler {sigmas}" + ) + return sigmas, alphas, alphas_prev + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, + which defines the cumulative product of (1-beta) over time from t = [0,1]. + :param num_diffusion_timesteps: the number of betas to produce. + :param alpha_bar: a lambda that takes an argument t from 0 to 1 and + produces the cumulative product of (1-beta) up to that + part of the diffusion process. + :param max_beta: the maximum beta to use; use values lower than 1 to + prevent singularities. + """ + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + return np.array(betas) + + +def extract_into_tensor(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def checkpoint(func, inputs, params, flag): + """ + Evaluate a function without caching intermediate activations, allowing for + reduced memory at the expense of extra compute in the backward pass. + :param func: the function to evaluate. + :param inputs: the argument sequence to pass to `func`. + :param params: a sequence of parameters `func` depends on but does not + explicitly take as arguments. + :param flag: if False, disable gradient checkpointing. + """ + if flag: + args = tuple(inputs) + tuple(params) + return CheckpointFunction.apply(func, len(inputs), *args) + else: + return func(*inputs) + + +class CheckpointFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, run_function, length, *args): + ctx.run_function = run_function + ctx.input_tensors = list(args[:length]) + ctx.input_params = list(args[length:]) + + with torch.no_grad(): + output_tensors = ctx.run_function(*ctx.input_tensors) + return output_tensors + + @staticmethod + def backward(ctx, *output_grads): + ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] + with torch.enable_grad(): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) + input_grads = torch.autograd.grad( + output_tensors, + ctx.input_tensors + ctx.input_params, + output_grads, + allow_unused=True, + ) + del ctx.input_tensors + del ctx.input_params + del output_tensors + return (None, None) + input_grads + + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=timesteps.device) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + else: + embedding = repeat(timesteps, "b -> b d", d=dim) + return embedding + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def scale_module(module, scale): + """ + Scale the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def normalization(channels): + """ + Make a standard normalization layer. + :param channels: number of input channels. + :return: an nn.Module for normalization. + """ + # return GroupNorm32(32, channels) + return nn.GroupNorm(32, channels) + + +# PyTorch 1.7 has SiLU, but we support PyTorch 1.5. +class SiLU(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + + +class GroupNorm32(nn.GroupNorm): + def forward(self, x): + return super().forward(x.float()).type(x.dtype) + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def linear(*args, **kwargs): + """ + Create a linear module. + """ + return nn.Linear(*args, **kwargs) + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +class HybridConditioner(nn.Module): + def __init__(self, c_concat_config, c_crossattn_config): + super().__init__() + self.concat_conditioner = instantiate_from_config(c_concat_config) + self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) + + def forward(self, c_concat, c_crossattn): + c_concat = self.concat_conditioner(c_concat) + c_crossattn = self.crossattn_conditioner(c_crossattn) + return {"c_concat": [c_concat], "c_crossattn": [c_crossattn]} + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat( + shape[0], *((1,) * (len(shape) - 1)) + ) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() diff --git a/extern/ldm_zero123/modules/distributions/__init__.py b/extern/ldm_zero123/modules/distributions/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/extern/ldm_zero123/modules/distributions/distributions.py b/extern/ldm_zero123/modules/distributions/distributions.py new file mode 100755 index 0000000..016be35 --- /dev/null +++ b/extern/ldm_zero123/modules/distributions/distributions.py @@ -0,0 +1,102 @@ +import numpy as np +import torch + + +class AbstractDistribution: + def sample(self): + raise NotImplementedError() + + def mode(self): + raise NotImplementedError() + + +class DiracDistribution(AbstractDistribution): + def __init__(self, value): + self.value = value + + def sample(self): + return self.value + + def mode(self): + return self.value + + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean).to( + device=self.parameters.device + ) + + def sample(self): + x = self.mean + self.std * torch.randn(self.mean.shape).to( + device=self.parameters.device + ) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.0]) + else: + if other is None: + return 0.5 * torch.sum( + torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, + dim=[1, 2, 3], + ) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var + - 1.0 + - self.logvar + + other.logvar, + dim=[1, 2, 3], + ) + + def nll(self, sample, dims=[1, 2, 3]): + if self.deterministic: + return torch.Tensor([0.0]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims, + ) + + def mode(self): + return self.mean + + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 + Compute the KL divergence between two gaussians. + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, torch.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for torch.exp(). + logvar1, logvar2 = [ + x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) + for x in (logvar1, logvar2) + ] + + return 0.5 * ( + -1.0 + + logvar2 + - logvar1 + + torch.exp(logvar1 - logvar2) + + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) + ) diff --git a/extern/ldm_zero123/modules/ema.py b/extern/ldm_zero123/modules/ema.py new file mode 100755 index 0000000..880ca3d --- /dev/null +++ b/extern/ldm_zero123/modules/ema.py @@ -0,0 +1,82 @@ +import torch +from torch import nn + + +class LitEma(nn.Module): + def __init__(self, model, decay=0.9999, use_num_upates=True): + super().__init__() + if decay < 0.0 or decay > 1.0: + raise ValueError("Decay must be between 0 and 1") + + self.m_name2s_name = {} + self.register_buffer("decay", torch.tensor(decay, dtype=torch.float32)) + self.register_buffer( + "num_updates", + torch.tensor(0, dtype=torch.int) + if use_num_upates + else torch.tensor(-1, dtype=torch.int), + ) + + for name, p in model.named_parameters(): + if p.requires_grad: + # remove as '.'-character is not allowed in buffers + s_name = name.replace(".", "") + self.m_name2s_name.update({name: s_name}) + self.register_buffer(s_name, p.clone().detach().data) + + self.collected_params = [] + + def forward(self, model): + decay = self.decay + + if self.num_updates >= 0: + self.num_updates += 1 + decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates)) + + one_minus_decay = 1.0 - decay + + with torch.no_grad(): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + + for key in m_param: + if m_param[key].requires_grad: + sname = self.m_name2s_name[key] + shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) + shadow_params[sname].sub_( + one_minus_decay * (shadow_params[sname] - m_param[key]) + ) + else: + assert not key in self.m_name2s_name + + def copy_to(self, model): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + for key in m_param: + if m_param[key].requires_grad: + m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) + else: + assert not key in self.m_name2s_name + + def store(self, parameters): + """ + Save the current parameters for restoring later. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + temporarily stored. + """ + self.collected_params = [param.clone() for param in parameters] + + def restore(self, parameters): + """ + Restore the parameters stored with the `store` method. + Useful to validate the model with EMA parameters without affecting the + original optimization process. Store the parameters before the + `copy_to` method. After validation (or model saving), use this to + restore the former parameters. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + updated with the stored parameters. + """ + for c_param, param in zip(self.collected_params, parameters): + param.data.copy_(c_param.data) diff --git a/extern/ldm_zero123/modules/encoders/__init__.py b/extern/ldm_zero123/modules/encoders/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/extern/ldm_zero123/modules/encoders/modules.py b/extern/ldm_zero123/modules/encoders/modules.py new file mode 100755 index 0000000..a88c36a --- /dev/null +++ b/extern/ldm_zero123/modules/encoders/modules.py @@ -0,0 +1,712 @@ +from functools import partial + +import clip +import kornia +import numpy as np +import torch +import torch.nn as nn + +from extern.ldm_zero123.modules.x_transformer import ( # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test + Encoder, + TransformerWrapper, +) +from extern.ldm_zero123.util import default + + +class AbstractEncoder(nn.Module): + def __init__(self): + super().__init__() + + def encode(self, *args, **kwargs): + raise NotImplementedError + + +class IdentityEncoder(AbstractEncoder): + def encode(self, x): + return x + + +class FaceClipEncoder(AbstractEncoder): + def __init__(self, augment=True, retreival_key=None): + super().__init__() + self.encoder = FrozenCLIPImageEmbedder() + self.augment = augment + self.retreival_key = retreival_key + + def forward(self, img): + encodings = [] + with torch.no_grad(): + x_offset = 125 + if self.retreival_key: + # Assumes retrieved image are packed into the second half of channels + face = img[:, 3:, 190:440, x_offset : (512 - x_offset)] + other = img[:, :3, ...].clone() + else: + face = img[:, :, 190:440, x_offset : (512 - x_offset)] + other = img.clone() + + if self.augment: + face = K.RandomHorizontalFlip()(face) + + other[:, :, 190:440, x_offset : (512 - x_offset)] *= 0 + encodings = [ + self.encoder.encode(face), + self.encoder.encode(other), + ] + + return torch.cat(encodings, dim=1) + + def encode(self, img): + if isinstance(img, list): + # Uncondition + return torch.zeros( + (1, 2, 768), device=self.encoder.model.visual.conv1.weight.device + ) + + return self(img) + + +class FaceIdClipEncoder(AbstractEncoder): + def __init__(self): + super().__init__() + self.encoder = FrozenCLIPImageEmbedder() + for p in self.encoder.parameters(): + p.requires_grad = False + self.id = FrozenFaceEncoder( + "/home/jpinkney/code/stable-diffusion/model_ir_se50.pth", augment=True + ) + + def forward(self, img): + encodings = [] + with torch.no_grad(): + face = kornia.geometry.resize( + img, (256, 256), interpolation="bilinear", align_corners=True + ) + + other = img.clone() + other[:, :, 184:452, 122:396] *= 0 + encodings = [ + self.id.encode(face), + self.encoder.encode(other), + ] + + return torch.cat(encodings, dim=1) + + def encode(self, img): + if isinstance(img, list): + # Uncondition + return torch.zeros( + (1, 2, 768), device=self.encoder.model.visual.conv1.weight.device + ) + + return self(img) + + +class ClassEmbedder(nn.Module): + def __init__(self, embed_dim, n_classes=1000, key="class"): + super().__init__() + self.key = key + self.embedding = nn.Embedding(n_classes, embed_dim) + + def forward(self, batch, key=None): + if key is None: + key = self.key + # this is for use in crossattn + c = batch[key][:, None] + c = self.embedding(c) + return c + + +class TransformerEmbedder(AbstractEncoder): + """Some transformer encoder layers""" + + def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"): + super().__init__() + self.device = device + self.transformer = TransformerWrapper( + num_tokens=vocab_size, + max_seq_len=max_seq_len, + attn_layers=Encoder(dim=n_embed, depth=n_layer), + ) + + def forward(self, tokens): + tokens = tokens.to(self.device) # meh + z = self.transformer(tokens, return_embeddings=True) + return z + + def encode(self, x): + return self(x) + + +class BERTTokenizer(AbstractEncoder): + """Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)""" + + def __init__(self, device="cuda", vq_interface=True, max_length=77): + super().__init__() + from transformers import BertTokenizerFast # TODO: add to reuquirements + + self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") + self.device = device + self.vq_interface = vq_interface + self.max_length = max_length + + def forward(self, text): + batch_encoding = self.tokenizer( + text, + truncation=True, + max_length=self.max_length, + return_length=True, + return_overflowing_tokens=False, + padding="max_length", + return_tensors="pt", + ) + tokens = batch_encoding["input_ids"].to(self.device) + return tokens + + @torch.no_grad() + def encode(self, text): + tokens = self(text) + if not self.vq_interface: + return tokens + return None, None, [None, None, tokens] + + def decode(self, text): + return text + + +class BERTEmbedder(AbstractEncoder): + """Uses the BERT tokenizr model and add some transformer encoder layers""" + + def __init__( + self, + n_embed, + n_layer, + vocab_size=30522, + max_seq_len=77, + device="cuda", + use_tokenizer=True, + embedding_dropout=0.0, + ): + super().__init__() + self.use_tknz_fn = use_tokenizer + if self.use_tknz_fn: + self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len) + self.device = device + self.transformer = TransformerWrapper( + num_tokens=vocab_size, + max_seq_len=max_seq_len, + attn_layers=Encoder(dim=n_embed, depth=n_layer), + emb_dropout=embedding_dropout, + ) + + def forward(self, text): + if self.use_tknz_fn: + tokens = self.tknz_fn(text) # .to(self.device) + else: + tokens = text + z = self.transformer(tokens, return_embeddings=True) + return z + + def encode(self, text): + # output of length 77 + return self(text) + + +from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +class FrozenT5Embedder(AbstractEncoder): + """Uses the T5 transformer encoder for text""" + + def __init__( + self, version="google/t5-v1_1-large", device="cuda", max_length=77 + ): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl + super().__init__() + self.tokenizer = T5Tokenizer.from_pretrained(version) + self.transformer = T5EncoderModel.from_pretrained(version) + self.device = device + self.max_length = max_length # TODO: typical value? + self.freeze() + + def freeze(self): + self.transformer = self.transformer.eval() + # self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + batch_encoding = self.tokenizer( + text, + truncation=True, + max_length=self.max_length, + return_length=True, + return_overflowing_tokens=False, + padding="max_length", + return_tensors="pt", + ) + tokens = batch_encoding["input_ids"].to(self.device) + outputs = self.transformer(input_ids=tokens) + + z = outputs.last_hidden_state + return z + + def encode(self, text): + return self(text) + + +import kornia.augmentation as K + +from extern.ldm_zero123.thirdp.psp.id_loss import IDFeatures + + +class FrozenFaceEncoder(AbstractEncoder): + def __init__(self, model_path, augment=False): + super().__init__() + self.loss_fn = IDFeatures(model_path) + # face encoder is frozen + for p in self.loss_fn.parameters(): + p.requires_grad = False + # Mapper is trainable + self.mapper = torch.nn.Linear(512, 768) + p = 0.25 + if augment: + self.augment = K.AugmentationSequential( + K.RandomHorizontalFlip(p=0.5), + K.RandomEqualize(p=p), + # K.RandomPlanckianJitter(p=p), + # K.RandomPlasmaBrightness(p=p), + # K.RandomPlasmaContrast(p=p), + # K.ColorJiggle(0.02, 0.2, 0.2, p=p), + ) + else: + self.augment = False + + def forward(self, img): + if isinstance(img, list): + # Uncondition + return torch.zeros((1, 1, 768), device=self.mapper.weight.device) + + if self.augment is not None: + # Transforms require 0-1 + img = self.augment((img + 1) / 2) + img = 2 * img - 1 + + feat = self.loss_fn(img, crop=True) + feat = self.mapper(feat.unsqueeze(1)) + return feat + + def encode(self, img): + return self(img) + + +class FrozenCLIPEmbedder(AbstractEncoder): + """Uses the CLIP transformer encoder for text (from huggingface)""" + + def __init__( + self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77 + ): # clip-vit-base-patch32 + super().__init__() + self.tokenizer = CLIPTokenizer.from_pretrained(version) + self.transformer = CLIPTextModel.from_pretrained(version) + self.device = device + self.max_length = max_length # TODO: typical value? + self.freeze() + + def freeze(self): + self.transformer = self.transformer.eval() + # self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + batch_encoding = self.tokenizer( + text, + truncation=True, + max_length=self.max_length, + return_length=True, + return_overflowing_tokens=False, + padding="max_length", + return_tensors="pt", + ) + tokens = batch_encoding["input_ids"].to(self.device) + outputs = self.transformer(input_ids=tokens) + + z = outputs.last_hidden_state + return z + + def encode(self, text): + return self(text) + + +import torch.nn.functional as F +from transformers import CLIPVisionModel + + +class ClipImageProjector(AbstractEncoder): + """ + Uses the CLIP image encoder. + """ + + def __init__( + self, version="openai/clip-vit-large-patch14", max_length=77 + ): # clip-vit-base-patch32 + super().__init__() + self.model = CLIPVisionModel.from_pretrained(version) + self.model.train() + self.max_length = max_length # TODO: typical value? + self.antialias = True + self.mapper = torch.nn.Linear(1024, 768) + self.register_buffer( + "mean", torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False + ) + self.register_buffer( + "std", torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False + ) + null_cond = self.get_null_cond(version, max_length) + self.register_buffer("null_cond", null_cond) + + @torch.no_grad() + def get_null_cond(self, version, max_length): + device = self.mean.device + embedder = FrozenCLIPEmbedder( + version=version, device=device, max_length=max_length + ) + null_cond = embedder([""]) + return null_cond + + def preprocess(self, x): + # Expects inputs in the range -1, 1 + x = kornia.geometry.resize( + x, + (224, 224), + interpolation="bicubic", + align_corners=True, + antialias=self.antialias, + ) + x = (x + 1.0) / 2.0 + # renormalize according to clip + x = kornia.enhance.normalize(x, self.mean, self.std) + return x + + def forward(self, x): + if isinstance(x, list): + return self.null_cond + # x is assumed to be in range [-1,1] + x = self.preprocess(x) + outputs = self.model(pixel_values=x) + last_hidden_state = outputs.last_hidden_state + last_hidden_state = self.mapper(last_hidden_state) + return F.pad( + last_hidden_state, + [0, 0, 0, self.max_length - last_hidden_state.shape[1], 0, 0], + ) + + def encode(self, im): + return self(im) + + +class ProjectedFrozenCLIPEmbedder(AbstractEncoder): + def __init__( + self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77 + ): # clip-vit-base-patch32 + super().__init__() + self.embedder = FrozenCLIPEmbedder( + version=version, device=device, max_length=max_length + ) + self.projection = torch.nn.Linear(768, 768) + + def forward(self, text): + z = self.embedder(text) + return self.projection(z) + + def encode(self, text): + return self(text) + + +class FrozenCLIPImageEmbedder(AbstractEncoder): + """ + Uses the CLIP image encoder. + Not actually frozen... If you want that set cond_stage_trainable=False in cfg + """ + + def __init__( + self, + model="ViT-L/14", + jit=False, + device="cpu", + antialias=False, + ): + super().__init__() + self.model, _ = clip.load(name=model, device=device, jit=jit) + # We don't use the text part so delete it + del self.model.transformer + self.antialias = antialias + self.register_buffer( + "mean", torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False + ) + self.register_buffer( + "std", torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False + ) + + def preprocess(self, x): + # Expects inputs in the range -1, 1 + x = kornia.geometry.resize( + x, + (224, 224), + interpolation="bicubic", + align_corners=True, + antialias=self.antialias, + ) + x = (x + 1.0) / 2.0 + # renormalize according to clip + x = kornia.enhance.normalize(x, self.mean, self.std) + return x + + def forward(self, x): + # x is assumed to be in range [-1,1] + if isinstance(x, list): + # [""] denotes condition dropout for ucg + device = self.model.visual.conv1.weight.device + return torch.zeros(1, 768, device=device) + return self.model.encode_image(self.preprocess(x)).float() + + def encode(self, im): + return self(im).unsqueeze(1) + + +import random + +from torchvision import transforms + + +class FrozenCLIPImageMutliEmbedder(AbstractEncoder): + """ + Uses the CLIP image encoder. + Not actually frozen... If you want that set cond_stage_trainable=False in cfg + """ + + def __init__( + self, + model="ViT-L/14", + jit=False, + device="cpu", + antialias=True, + max_crops=5, + ): + super().__init__() + self.model, _ = clip.load(name=model, device=device, jit=jit) + # We don't use the text part so delete it + del self.model.transformer + self.antialias = antialias + self.register_buffer( + "mean", torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False + ) + self.register_buffer( + "std", torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False + ) + self.max_crops = max_crops + + def preprocess(self, x): + # Expects inputs in the range -1, 1 + randcrop = transforms.RandomResizedCrop(224, scale=(0.085, 1.0), ratio=(1, 1)) + max_crops = self.max_crops + patches = [] + crops = [randcrop(x) for _ in range(max_crops)] + patches.extend(crops) + x = torch.cat(patches, dim=0) + x = (x + 1.0) / 2.0 + # renormalize according to clip + x = kornia.enhance.normalize(x, self.mean, self.std) + return x + + def forward(self, x): + # x is assumed to be in range [-1,1] + if isinstance(x, list): + # [""] denotes condition dropout for ucg + device = self.model.visual.conv1.weight.device + return torch.zeros(1, self.max_crops, 768, device=device) + batch_tokens = [] + for im in x: + patches = self.preprocess(im.unsqueeze(0)) + tokens = self.model.encode_image(patches).float() + for t in tokens: + if random.random() < 0.1: + t *= 0 + batch_tokens.append(tokens.unsqueeze(0)) + + return torch.cat(batch_tokens, dim=0) + + def encode(self, im): + return self(im) + + +class SpatialRescaler(nn.Module): + def __init__( + self, + n_stages=1, + method="bilinear", + multiplier=0.5, + in_channels=3, + out_channels=None, + bias=False, + ): + super().__init__() + self.n_stages = n_stages + assert self.n_stages >= 0 + assert method in [ + "nearest", + "linear", + "bilinear", + "trilinear", + "bicubic", + "area", + ] + self.multiplier = multiplier + self.interpolator = partial(torch.nn.functional.interpolate, mode=method) + self.remap_output = out_channels is not None + if self.remap_output: + print( + f"Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing." + ) + self.channel_mapper = nn.Conv2d(in_channels, out_channels, 1, bias=bias) + + def forward(self, x): + for stage in range(self.n_stages): + x = self.interpolator(x, scale_factor=self.multiplier) + + if self.remap_output: + x = self.channel_mapper(x) + return x + + def encode(self, x): + return self(x) + + +from extern.ldm_zero123.modules.diffusionmodules.util import ( + extract_into_tensor, + make_beta_schedule, + noise_like, +) +from extern.ldm_zero123.util import instantiate_from_config + + +class LowScaleEncoder(nn.Module): + def __init__( + self, + model_config, + linear_start, + linear_end, + timesteps=1000, + max_noise_level=250, + output_size=64, + scale_factor=1.0, + ): + super().__init__() + self.max_noise_level = max_noise_level + self.model = instantiate_from_config(model_config) + self.augmentation_schedule = self.register_schedule( + timesteps=timesteps, linear_start=linear_start, linear_end=linear_end + ) + self.out_size = output_size + self.scale_factor = scale_factor + + def register_schedule( + self, + beta_schedule="linear", + timesteps=1000, + linear_start=1e-4, + linear_end=2e-2, + cosine_s=8e-3, + ): + betas = make_beta_schedule( + beta_schedule, + timesteps, + linear_start=linear_start, + linear_end=linear_end, + cosine_s=cosine_s, + ) + alphas = 1.0 - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1]) + + (timesteps,) = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + assert ( + alphas_cumprod.shape[0] == self.num_timesteps + ), "alphas have to be defined for each timestep" + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer("betas", to_torch(betas)) + self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod)) + self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer( + "sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod)) + ) + self.register_buffer( + "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod)) + ) + self.register_buffer( + "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod)) + ) + self.register_buffer( + "sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1)) + ) + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) + * noise + ) + + def forward(self, x): + z = self.model.encode(x).sample() + z = z * self.scale_factor + noise_level = torch.randint( + 0, self.max_noise_level, (x.shape[0],), device=x.device + ).long() + z = self.q_sample(z, noise_level) + if self.out_size is not None: + z = torch.nn.functional.interpolate( + z, size=self.out_size, mode="nearest" + ) # TODO: experiment with mode + # z = z.repeat_interleave(2, -2).repeat_interleave(2, -1) + return z, noise_level + + def decode(self, z): + z = z / self.scale_factor + return self.model.decode(z) + + +if __name__ == "__main__": + from extern.ldm_zero123.util import count_params + + sentences = [ + "a hedgehog drinking a whiskey", + "der mond ist aufgegangen", + "Ein Satz mit vielen Sonderzeichen: äöü ß ?! : 'xx-y/@s'", + ] + model = FrozenT5Embedder(version="google/t5-v1_1-xl").cuda() + count_params(model, True) + z = model(sentences) + print(z.shape) + + model = FrozenCLIPEmbedder().cuda() + count_params(model, True) + z = model(sentences) + print(z.shape) + + print("done.") diff --git a/extern/ldm_zero123/modules/evaluate/adm_evaluator.py b/extern/ldm_zero123/modules/evaluate/adm_evaluator.py new file mode 100755 index 0000000..6b70eda --- /dev/null +++ b/extern/ldm_zero123/modules/evaluate/adm_evaluator.py @@ -0,0 +1,703 @@ +import argparse +import io +import os +import random +import warnings +import zipfile +from abc import ABC, abstractmethod +from contextlib import contextmanager +from functools import partial +from multiprocessing import cpu_count +from multiprocessing.pool import ThreadPool +from typing import Iterable, Optional, Tuple + +import numpy as np +import requests +import tensorflow.compat.v1 as tf +import yaml +from scipy import linalg +from tqdm.auto import tqdm + +INCEPTION_V3_URL = "https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/classify_image_graph_def.pb" +INCEPTION_V3_PATH = "classify_image_graph_def.pb" + +FID_POOL_NAME = "pool_3:0" +FID_SPATIAL_NAME = "mixed_6/conv:0" + +REQUIREMENTS = ( + f"This script has the following requirements: \n" + "tensorflow-gpu>=2.0" + "\n" + "scipy" + "\n" + "requests" + "\n" + "tqdm" +) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--ref_batch", help="path to reference batch npz file") + parser.add_argument("--sample_batch", help="path to sample batch npz file") + args = parser.parse_args() + + config = tf.ConfigProto( + allow_soft_placement=True # allows DecodeJpeg to run on CPU in Inception graph + ) + config.gpu_options.allow_growth = True + evaluator = Evaluator(tf.Session(config=config)) + + print("warming up TensorFlow...") + # This will cause TF to print a bunch of verbose stuff now rather + # than after the next print(), to help prevent confusion. + evaluator.warmup() + + print("computing reference batch activations...") + ref_acts = evaluator.read_activations(args.ref_batch) + print("computing/reading reference batch statistics...") + ref_stats, ref_stats_spatial = evaluator.read_statistics(args.ref_batch, ref_acts) + + print("computing sample batch activations...") + sample_acts = evaluator.read_activations(args.sample_batch) + print("computing/reading sample batch statistics...") + sample_stats, sample_stats_spatial = evaluator.read_statistics( + args.sample_batch, sample_acts + ) + + print("Computing evaluations...") + is_ = evaluator.compute_inception_score(sample_acts[0]) + print("Inception Score:", is_) + fid = sample_stats.frechet_distance(ref_stats) + print("FID:", fid) + sfid = sample_stats_spatial.frechet_distance(ref_stats_spatial) + print("sFID:", sfid) + prec, recall = evaluator.compute_prec_recall(ref_acts[0], sample_acts[0]) + print("Precision:", prec) + print("Recall:", recall) + + savepath = "/".join(args.sample_batch.split("/")[:-1]) + results_file = os.path.join(savepath, "evaluation_metrics.yaml") + print(f'Saving evaluation results to "{results_file}"') + + results = { + "IS": is_, + "FID": fid, + "sFID": sfid, + "Precision:": prec, + "Recall": recall, + } + + with open(results_file, "w") as f: + yaml.dump(results, f, default_flow_style=False) + + +class InvalidFIDException(Exception): + pass + + +class FIDStatistics: + def __init__(self, mu: np.ndarray, sigma: np.ndarray): + self.mu = mu + self.sigma = sigma + + def frechet_distance(self, other, eps=1e-6): + """ + Compute the Frechet distance between two sets of statistics. + """ + # https://github.com/bioinf-jku/TTUR/blob/73ab375cdf952a12686d9aa7978567771084da42/fid.py#L132 + mu1, sigma1 = self.mu, self.sigma + mu2, sigma2 = other.mu, other.sigma + + mu1 = np.atleast_1d(mu1) + mu2 = np.atleast_1d(mu2) + + sigma1 = np.atleast_2d(sigma1) + sigma2 = np.atleast_2d(sigma2) + + assert ( + mu1.shape == mu2.shape + ), f"Training and test mean vectors have different lengths: {mu1.shape}, {mu2.shape}" + assert ( + sigma1.shape == sigma2.shape + ), f"Training and test covariances have different dimensions: {sigma1.shape}, {sigma2.shape}" + + diff = mu1 - mu2 + + # product might be almost singular + covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) + if not np.isfinite(covmean).all(): + msg = ( + "fid calculation produces singular product; adding %s to diagonal of cov estimates" + % eps + ) + warnings.warn(msg) + offset = np.eye(sigma1.shape[0]) * eps + covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) + + # numerical error might give slight imaginary component + if np.iscomplexobj(covmean): + if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): + m = np.max(np.abs(covmean.imag)) + raise ValueError("Imaginary component {}".format(m)) + covmean = covmean.real + + tr_covmean = np.trace(covmean) + + return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean + + +class Evaluator: + def __init__( + self, + session, + batch_size=64, + softmax_batch_size=512, + ): + self.sess = session + self.batch_size = batch_size + self.softmax_batch_size = softmax_batch_size + self.manifold_estimator = ManifoldEstimator(session) + with self.sess.graph.as_default(): + self.image_input = tf.placeholder(tf.float32, shape=[None, None, None, 3]) + self.softmax_input = tf.placeholder(tf.float32, shape=[None, 2048]) + self.pool_features, self.spatial_features = _create_feature_graph( + self.image_input + ) + self.softmax = _create_softmax_graph(self.softmax_input) + + def warmup(self): + self.compute_activations(np.zeros([1, 8, 64, 64, 3])) + + def read_activations(self, npz_path: str) -> Tuple[np.ndarray, np.ndarray]: + with open_npz_array(npz_path, "arr_0") as reader: + return self.compute_activations(reader.read_batches(self.batch_size)) + + def compute_activations( + self, batches: Iterable[np.ndarray], silent=False + ) -> Tuple[np.ndarray, np.ndarray]: + """ + Compute image features for downstream evals. + + :param batches: a iterator over NHWC numpy arrays in [0, 255]. + :return: a tuple of numpy arrays of shape [N x X], where X is a feature + dimension. The tuple is (pool_3, spatial). + """ + preds = [] + spatial_preds = [] + it = batches if silent else tqdm(batches) + for batch in it: + batch = batch.astype(np.float32) + pred, spatial_pred = self.sess.run( + [self.pool_features, self.spatial_features], {self.image_input: batch} + ) + preds.append(pred.reshape([pred.shape[0], -1])) + spatial_preds.append(spatial_pred.reshape([spatial_pred.shape[0], -1])) + return ( + np.concatenate(preds, axis=0), + np.concatenate(spatial_preds, axis=0), + ) + + def read_statistics( + self, npz_path: str, activations: Tuple[np.ndarray, np.ndarray] + ) -> Tuple[FIDStatistics, FIDStatistics]: + obj = np.load(npz_path) + if "mu" in list(obj.keys()): + return FIDStatistics(obj["mu"], obj["sigma"]), FIDStatistics( + obj["mu_s"], obj["sigma_s"] + ) + return tuple(self.compute_statistics(x) for x in activations) + + def compute_statistics(self, activations: np.ndarray) -> FIDStatistics: + mu = np.mean(activations, axis=0) + sigma = np.cov(activations, rowvar=False) + return FIDStatistics(mu, sigma) + + def compute_inception_score( + self, activations: np.ndarray, split_size: int = 5000 + ) -> float: + softmax_out = [] + for i in range(0, len(activations), self.softmax_batch_size): + acts = activations[i : i + self.softmax_batch_size] + softmax_out.append( + self.sess.run(self.softmax, feed_dict={self.softmax_input: acts}) + ) + preds = np.concatenate(softmax_out, axis=0) + # https://github.com/openai/improved-gan/blob/4f5d1ec5c16a7eceb206f42bfc652693601e1d5c/inception_score/model.py#L46 + scores = [] + for i in range(0, len(preds), split_size): + part = preds[i : i + split_size] + kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0))) + kl = np.mean(np.sum(kl, 1)) + scores.append(np.exp(kl)) + return float(np.mean(scores)) + + def compute_prec_recall( + self, activations_ref: np.ndarray, activations_sample: np.ndarray + ) -> Tuple[float, float]: + radii_1 = self.manifold_estimator.manifold_radii(activations_ref) + radii_2 = self.manifold_estimator.manifold_radii(activations_sample) + pr = self.manifold_estimator.evaluate_pr( + activations_ref, radii_1, activations_sample, radii_2 + ) + return (float(pr[0][0]), float(pr[1][0])) + + +class ManifoldEstimator: + """ + A helper for comparing manifolds of feature vectors. + + Adapted from https://github.com/kynkaat/improved-precision-and-recall-metric/blob/f60f25e5ad933a79135c783fcda53de30f42c9b9/precision_recall.py#L57 + """ + + def __init__( + self, + session, + row_batch_size=10000, + col_batch_size=10000, + nhood_sizes=(3,), + clamp_to_percentile=None, + eps=1e-5, + ): + """ + Estimate the manifold of given feature vectors. + + :param session: the TensorFlow session. + :param row_batch_size: row batch size to compute pairwise distances + (parameter to trade-off between memory usage and performance). + :param col_batch_size: column batch size to compute pairwise distances. + :param nhood_sizes: number of neighbors used to estimate the manifold. + :param clamp_to_percentile: prune hyperspheres that have radius larger than + the given percentile. + :param eps: small number for numerical stability. + """ + self.distance_block = DistanceBlock(session) + self.row_batch_size = row_batch_size + self.col_batch_size = col_batch_size + self.nhood_sizes = nhood_sizes + self.num_nhoods = len(nhood_sizes) + self.clamp_to_percentile = clamp_to_percentile + self.eps = eps + + def warmup(self): + feats, radii = ( + np.zeros([1, 2048], dtype=np.float32), + np.zeros([1, 1], dtype=np.float32), + ) + self.evaluate_pr(feats, radii, feats, radii) + + def manifold_radii(self, features: np.ndarray) -> np.ndarray: + num_images = len(features) + + # Estimate manifold of features by calculating distances to k-NN of each sample. + radii = np.zeros([num_images, self.num_nhoods], dtype=np.float32) + distance_batch = np.zeros([self.row_batch_size, num_images], dtype=np.float32) + seq = np.arange(max(self.nhood_sizes) + 1, dtype=np.int32) + + for begin1 in range(0, num_images, self.row_batch_size): + end1 = min(begin1 + self.row_batch_size, num_images) + row_batch = features[begin1:end1] + + for begin2 in range(0, num_images, self.col_batch_size): + end2 = min(begin2 + self.col_batch_size, num_images) + col_batch = features[begin2:end2] + + # Compute distances between batches. + distance_batch[ + 0 : end1 - begin1, begin2:end2 + ] = self.distance_block.pairwise_distances(row_batch, col_batch) + + # Find the k-nearest neighbor from the current batch. + radii[begin1:end1, :] = np.concatenate( + [ + x[:, self.nhood_sizes] + for x in _numpy_partition( + distance_batch[0 : end1 - begin1, :], seq, axis=1 + ) + ], + axis=0, + ) + + if self.clamp_to_percentile is not None: + max_distances = np.percentile(radii, self.clamp_to_percentile, axis=0) + radii[radii > max_distances] = 0 + return radii + + def evaluate( + self, features: np.ndarray, radii: np.ndarray, eval_features: np.ndarray + ): + """ + Evaluate if new feature vectors are at the manifold. + """ + num_eval_images = eval_features.shape[0] + num_ref_images = radii.shape[0] + distance_batch = np.zeros( + [self.row_batch_size, num_ref_images], dtype=np.float32 + ) + batch_predictions = np.zeros([num_eval_images, self.num_nhoods], dtype=np.int32) + max_realism_score = np.zeros([num_eval_images], dtype=np.float32) + nearest_indices = np.zeros([num_eval_images], dtype=np.int32) + + for begin1 in range(0, num_eval_images, self.row_batch_size): + end1 = min(begin1 + self.row_batch_size, num_eval_images) + feature_batch = eval_features[begin1:end1] + + for begin2 in range(0, num_ref_images, self.col_batch_size): + end2 = min(begin2 + self.col_batch_size, num_ref_images) + ref_batch = features[begin2:end2] + + distance_batch[ + 0 : end1 - begin1, begin2:end2 + ] = self.distance_block.pairwise_distances(feature_batch, ref_batch) + + # From the minibatch of new feature vectors, determine if they are in the estimated manifold. + # If a feature vector is inside a hypersphere of some reference sample, then + # the new sample lies at the estimated manifold. + # The radii of the hyperspheres are determined from distances of neighborhood size k. + samples_in_manifold = distance_batch[0 : end1 - begin1, :, None] <= radii + batch_predictions[begin1:end1] = np.any(samples_in_manifold, axis=1).astype( + np.int32 + ) + + max_realism_score[begin1:end1] = np.max( + radii[:, 0] / (distance_batch[0 : end1 - begin1, :] + self.eps), axis=1 + ) + nearest_indices[begin1:end1] = np.argmin( + distance_batch[0 : end1 - begin1, :], axis=1 + ) + + return { + "fraction": float(np.mean(batch_predictions)), + "batch_predictions": batch_predictions, + "max_realisim_score": max_realism_score, + "nearest_indices": nearest_indices, + } + + def evaluate_pr( + self, + features_1: np.ndarray, + radii_1: np.ndarray, + features_2: np.ndarray, + radii_2: np.ndarray, + ) -> Tuple[np.ndarray, np.ndarray]: + """ + Evaluate precision and recall efficiently. + + :param features_1: [N1 x D] feature vectors for reference batch. + :param radii_1: [N1 x K1] radii for reference vectors. + :param features_2: [N2 x D] feature vectors for the other batch. + :param radii_2: [N x K2] radii for other vectors. + :return: a tuple of arrays for (precision, recall): + - precision: an np.ndarray of length K1 + - recall: an np.ndarray of length K2 + """ + features_1_status = np.zeros([len(features_1), radii_2.shape[1]], dtype=np.bool) + features_2_status = np.zeros([len(features_2), radii_1.shape[1]], dtype=np.bool) + for begin_1 in range(0, len(features_1), self.row_batch_size): + end_1 = begin_1 + self.row_batch_size + batch_1 = features_1[begin_1:end_1] + for begin_2 in range(0, len(features_2), self.col_batch_size): + end_2 = begin_2 + self.col_batch_size + batch_2 = features_2[begin_2:end_2] + batch_1_in, batch_2_in = self.distance_block.less_thans( + batch_1, radii_1[begin_1:end_1], batch_2, radii_2[begin_2:end_2] + ) + features_1_status[begin_1:end_1] |= batch_1_in + features_2_status[begin_2:end_2] |= batch_2_in + return ( + np.mean(features_2_status.astype(np.float64), axis=0), + np.mean(features_1_status.astype(np.float64), axis=0), + ) + + +class DistanceBlock: + """ + Calculate pairwise distances between vectors. + + Adapted from https://github.com/kynkaat/improved-precision-and-recall-metric/blob/f60f25e5ad933a79135c783fcda53de30f42c9b9/precision_recall.py#L34 + """ + + def __init__(self, session): + self.session = session + + # Initialize TF graph to calculate pairwise distances. + with session.graph.as_default(): + self._features_batch1 = tf.placeholder(tf.float32, shape=[None, None]) + self._features_batch2 = tf.placeholder(tf.float32, shape=[None, None]) + distance_block_16 = _batch_pairwise_distances( + tf.cast(self._features_batch1, tf.float16), + tf.cast(self._features_batch2, tf.float16), + ) + self.distance_block = tf.cond( + tf.reduce_all(tf.math.is_finite(distance_block_16)), + lambda: tf.cast(distance_block_16, tf.float32), + lambda: _batch_pairwise_distances( + self._features_batch1, self._features_batch2 + ), + ) + + # Extra logic for less thans. + self._radii1 = tf.placeholder(tf.float32, shape=[None, None]) + self._radii2 = tf.placeholder(tf.float32, shape=[None, None]) + dist32 = tf.cast(self.distance_block, tf.float32)[..., None] + self._batch_1_in = tf.math.reduce_any(dist32 <= self._radii2, axis=1) + self._batch_2_in = tf.math.reduce_any( + dist32 <= self._radii1[:, None], axis=0 + ) + + def pairwise_distances(self, U, V): + """ + Evaluate pairwise distances between two batches of feature vectors. + """ + return self.session.run( + self.distance_block, + feed_dict={self._features_batch1: U, self._features_batch2: V}, + ) + + def less_thans(self, batch_1, radii_1, batch_2, radii_2): + return self.session.run( + [self._batch_1_in, self._batch_2_in], + feed_dict={ + self._features_batch1: batch_1, + self._features_batch2: batch_2, + self._radii1: radii_1, + self._radii2: radii_2, + }, + ) + + +def _batch_pairwise_distances(U, V): + """ + Compute pairwise distances between two batches of feature vectors. + """ + with tf.variable_scope("pairwise_dist_block"): + # Squared norms of each row in U and V. + norm_u = tf.reduce_sum(tf.square(U), 1) + norm_v = tf.reduce_sum(tf.square(V), 1) + + # norm_u as a column and norm_v as a row vectors. + norm_u = tf.reshape(norm_u, [-1, 1]) + norm_v = tf.reshape(norm_v, [1, -1]) + + # Pairwise squared Euclidean distances. + D = tf.maximum(norm_u - 2 * tf.matmul(U, V, False, True) + norm_v, 0.0) + + return D + + +class NpzArrayReader(ABC): + @abstractmethod + def read_batch(self, batch_size: int) -> Optional[np.ndarray]: + pass + + @abstractmethod + def remaining(self) -> int: + pass + + def read_batches(self, batch_size: int) -> Iterable[np.ndarray]: + def gen_fn(): + while True: + batch = self.read_batch(batch_size) + if batch is None: + break + yield batch + + rem = self.remaining() + num_batches = rem // batch_size + int(rem % batch_size != 0) + return BatchIterator(gen_fn, num_batches) + + +class BatchIterator: + def __init__(self, gen_fn, length): + self.gen_fn = gen_fn + self.length = length + + def __len__(self): + return self.length + + def __iter__(self): + return self.gen_fn() + + +class StreamingNpzArrayReader(NpzArrayReader): + def __init__(self, arr_f, shape, dtype): + self.arr_f = arr_f + self.shape = shape + self.dtype = dtype + self.idx = 0 + + def read_batch(self, batch_size: int) -> Optional[np.ndarray]: + if self.idx >= self.shape[0]: + return None + + bs = min(batch_size, self.shape[0] - self.idx) + self.idx += bs + + if self.dtype.itemsize == 0: + return np.ndarray([bs, *self.shape[1:]], dtype=self.dtype) + + read_count = bs * np.prod(self.shape[1:]) + read_size = int(read_count * self.dtype.itemsize) + data = _read_bytes(self.arr_f, read_size, "array data") + return np.frombuffer(data, dtype=self.dtype).reshape([bs, *self.shape[1:]]) + + def remaining(self) -> int: + return max(0, self.shape[0] - self.idx) + + +class MemoryNpzArrayReader(NpzArrayReader): + def __init__(self, arr): + self.arr = arr + self.idx = 0 + + @classmethod + def load(cls, path: str, arr_name: str): + with open(path, "rb") as f: + arr = np.load(f)[arr_name] + return cls(arr) + + def read_batch(self, batch_size: int) -> Optional[np.ndarray]: + if self.idx >= self.arr.shape[0]: + return None + + res = self.arr[self.idx : self.idx + batch_size] + self.idx += batch_size + return res + + def remaining(self) -> int: + return max(0, self.arr.shape[0] - self.idx) + + +@contextmanager +def open_npz_array(path: str, arr_name: str) -> NpzArrayReader: + with _open_npy_file(path, arr_name) as arr_f: + version = np.lib.format.read_magic(arr_f) + if version == (1, 0): + header = np.lib.format.read_array_header_1_0(arr_f) + elif version == (2, 0): + header = np.lib.format.read_array_header_2_0(arr_f) + else: + yield MemoryNpzArrayReader.load(path, arr_name) + return + shape, fortran, dtype = header + if fortran or dtype.hasobject: + yield MemoryNpzArrayReader.load(path, arr_name) + else: + yield StreamingNpzArrayReader(arr_f, shape, dtype) + + +def _read_bytes(fp, size, error_template="ran out of data"): + """ + Copied from: https://github.com/numpy/numpy/blob/fb215c76967739268de71aa4bda55dd1b062bc2e/numpy/lib/format.py#L788-L886 + + Read from file-like object until size bytes are read. + Raises ValueError if not EOF is encountered before size bytes are read. + Non-blocking objects only supported if they derive from io objects. + Required as e.g. ZipExtFile in python 2.6 can return less data than + requested. + """ + data = bytes() + while True: + # io files (default in python3) return None or raise on + # would-block, python2 file will truncate, probably nothing can be + # done about that. note that regular files can't be non-blocking + try: + r = fp.read(size - len(data)) + data += r + if len(r) == 0 or len(data) == size: + break + except io.BlockingIOError: + pass + if len(data) != size: + msg = "EOF: reading %s, expected %d bytes got %d" + raise ValueError(msg % (error_template, size, len(data))) + else: + return data + + +@contextmanager +def _open_npy_file(path: str, arr_name: str): + with open(path, "rb") as f: + with zipfile.ZipFile(f, "r") as zip_f: + if f"{arr_name}.npy" not in zip_f.namelist(): + raise ValueError(f"missing {arr_name} in npz file") + with zip_f.open(f"{arr_name}.npy", "r") as arr_f: + yield arr_f + + +def _download_inception_model(): + if os.path.exists(INCEPTION_V3_PATH): + return + print("downloading InceptionV3 model...") + with requests.get(INCEPTION_V3_URL, stream=True) as r: + r.raise_for_status() + tmp_path = INCEPTION_V3_PATH + ".tmp" + with open(tmp_path, "wb") as f: + for chunk in tqdm(r.iter_content(chunk_size=8192)): + f.write(chunk) + os.rename(tmp_path, INCEPTION_V3_PATH) + + +def _create_feature_graph(input_batch): + _download_inception_model() + prefix = f"{random.randrange(2**32)}_{random.randrange(2**32)}" + with open(INCEPTION_V3_PATH, "rb") as f: + graph_def = tf.GraphDef() + graph_def.ParseFromString(f.read()) + pool3, spatial = tf.import_graph_def( + graph_def, + input_map={f"ExpandDims:0": input_batch}, + return_elements=[FID_POOL_NAME, FID_SPATIAL_NAME], + name=prefix, + ) + _update_shapes(pool3) + spatial = spatial[..., :7] + return pool3, spatial + + +def _create_softmax_graph(input_batch): + _download_inception_model() + prefix = f"{random.randrange(2**32)}_{random.randrange(2**32)}" + with open(INCEPTION_V3_PATH, "rb") as f: + graph_def = tf.GraphDef() + graph_def.ParseFromString(f.read()) + (matmul,) = tf.import_graph_def( + graph_def, return_elements=[f"softmax/logits/MatMul"], name=prefix + ) + w = matmul.inputs[1] + logits = tf.matmul(input_batch, w) + return tf.nn.softmax(logits) + + +def _update_shapes(pool3): + # https://github.com/bioinf-jku/TTUR/blob/73ab375cdf952a12686d9aa7978567771084da42/fid.py#L50-L63 + ops = pool3.graph.get_operations() + for op in ops: + for o in op.outputs: + shape = o.get_shape() + if shape._dims is not None: # pylint: disable=protected-access + # shape = [s.value for s in shape] TF 1.x + shape = [s for s in shape] # TF 2.x + new_shape = [] + for j, s in enumerate(shape): + if s == 1 and j == 0: + new_shape.append(None) + else: + new_shape.append(s) + o.__dict__["_shape_val"] = tf.TensorShape(new_shape) + return pool3 + + +def _numpy_partition(arr, kth, **kwargs): + num_workers = min(cpu_count(), len(arr)) + chunk_size = len(arr) // num_workers + extra = len(arr) % num_workers + + start_idx = 0 + batches = [] + for i in range(num_workers): + size = chunk_size + (1 if i < extra else 0) + batches.append(arr[start_idx : start_idx + size]) + start_idx += size + + with ThreadPool(num_workers) as pool: + return list(pool.map(partial(np.partition, kth=kth, **kwargs), batches)) + + +if __name__ == "__main__": + print(REQUIREMENTS) + main() diff --git a/extern/ldm_zero123/modules/evaluate/evaluate_perceptualsim.py b/extern/ldm_zero123/modules/evaluate/evaluate_perceptualsim.py new file mode 100755 index 0000000..023f8c3 --- /dev/null +++ b/extern/ldm_zero123/modules/evaluate/evaluate_perceptualsim.py @@ -0,0 +1,606 @@ +import argparse +import glob +import os +from collections import namedtuple + +import numpy as np +import torch +import torchvision.transforms as transforms +from PIL import Image +from torchvision import models +from tqdm import tqdm + +from extern.ldm_zero123.modules.evaluate.ssim import ssim + +transform = transforms.Compose([transforms.ToTensor()]) + + +def normalize_tensor(in_feat, eps=1e-10): + norm_factor = torch.sqrt(torch.sum(in_feat**2, dim=1)).view( + in_feat.size()[0], 1, in_feat.size()[2], in_feat.size()[3] + ) + return in_feat / (norm_factor.expand_as(in_feat) + eps) + + +def cos_sim(in0, in1): + in0_norm = normalize_tensor(in0) + in1_norm = normalize_tensor(in1) + N = in0.size()[0] + X = in0.size()[2] + Y = in0.size()[3] + + return torch.mean( + torch.mean(torch.sum(in0_norm * in1_norm, dim=1).view(N, 1, X, Y), dim=2).view( + N, 1, 1, Y + ), + dim=3, + ).view(N) + + +class squeezenet(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(squeezenet, self).__init__() + pretrained_features = models.squeezenet1_1(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.slice6 = torch.nn.Sequential() + self.slice7 = torch.nn.Sequential() + self.N_slices = 7 + for x in range(2): + self.slice1.add_module(str(x), pretrained_features[x]) + for x in range(2, 5): + self.slice2.add_module(str(x), pretrained_features[x]) + for x in range(5, 8): + self.slice3.add_module(str(x), pretrained_features[x]) + for x in range(8, 10): + self.slice4.add_module(str(x), pretrained_features[x]) + for x in range(10, 11): + self.slice5.add_module(str(x), pretrained_features[x]) + for x in range(11, 12): + self.slice6.add_module(str(x), pretrained_features[x]) + for x in range(12, 13): + self.slice7.add_module(str(x), pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1 = h + h = self.slice2(h) + h_relu2 = h + h = self.slice3(h) + h_relu3 = h + h = self.slice4(h) + h_relu4 = h + h = self.slice5(h) + h_relu5 = h + h = self.slice6(h) + h_relu6 = h + h = self.slice7(h) + h_relu7 = h + vgg_outputs = namedtuple( + "SqueezeOutputs", + ["relu1", "relu2", "relu3", "relu4", "relu5", "relu6", "relu7"], + ) + out = vgg_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5, h_relu6, h_relu7) + + return out + + +class alexnet(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(alexnet, self).__init__() + alexnet_pretrained_features = models.alexnet(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.N_slices = 5 + for x in range(2): + self.slice1.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(2, 5): + self.slice2.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(5, 8): + self.slice3.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(8, 10): + self.slice4.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(10, 12): + self.slice5.add_module(str(x), alexnet_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1 = h + h = self.slice2(h) + h_relu2 = h + h = self.slice3(h) + h_relu3 = h + h = self.slice4(h) + h_relu4 = h + h = self.slice5(h) + h_relu5 = h + alexnet_outputs = namedtuple( + "AlexnetOutputs", ["relu1", "relu2", "relu3", "relu4", "relu5"] + ) + out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5) + + return out + + +class vgg16(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(vgg16, self).__init__() + vgg_pretrained_features = models.vgg16(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.N_slices = 5 + for x in range(4): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(4, 9): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(9, 16): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(16, 23): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + for x in range(23, 30): + self.slice5.add_module(str(x), vgg_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1_2 = h + h = self.slice2(h) + h_relu2_2 = h + h = self.slice3(h) + h_relu3_3 = h + h = self.slice4(h) + h_relu4_3 = h + h = self.slice5(h) + h_relu5_3 = h + vgg_outputs = namedtuple( + "VggOutputs", + ["relu1_2", "relu2_2", "relu3_3", "relu4_3", "relu5_3"], + ) + out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3) + + return out + + +class resnet(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True, num=18): + super(resnet, self).__init__() + if num == 18: + self.net = models.resnet18(pretrained=pretrained) + elif num == 34: + self.net = models.resnet34(pretrained=pretrained) + elif num == 50: + self.net = models.resnet50(pretrained=pretrained) + elif num == 101: + self.net = models.resnet101(pretrained=pretrained) + elif num == 152: + self.net = models.resnet152(pretrained=pretrained) + self.N_slices = 5 + + self.conv1 = self.net.conv1 + self.bn1 = self.net.bn1 + self.relu = self.net.relu + self.maxpool = self.net.maxpool + self.layer1 = self.net.layer1 + self.layer2 = self.net.layer2 + self.layer3 = self.net.layer3 + self.layer4 = self.net.layer4 + + def forward(self, X): + h = self.conv1(X) + h = self.bn1(h) + h = self.relu(h) + h_relu1 = h + h = self.maxpool(h) + h = self.layer1(h) + h_conv2 = h + h = self.layer2(h) + h_conv3 = h + h = self.layer3(h) + h_conv4 = h + h = self.layer4(h) + h_conv5 = h + + outputs = namedtuple("Outputs", ["relu1", "conv2", "conv3", "conv4", "conv5"]) + out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5) + + return out + + +# Off-the-shelf deep network +class PNet(torch.nn.Module): + """Pre-trained network with all channels equally weighted by default""" + + def __init__(self, pnet_type="vgg", pnet_rand=False, use_gpu=True): + super(PNet, self).__init__() + + self.use_gpu = use_gpu + + self.pnet_type = pnet_type + self.pnet_rand = pnet_rand + + self.shift = torch.Tensor([-0.030, -0.088, -0.188]).view(1, 3, 1, 1) + self.scale = torch.Tensor([0.458, 0.448, 0.450]).view(1, 3, 1, 1) + + if self.pnet_type in ["vgg", "vgg16"]: + self.net = vgg16(pretrained=not self.pnet_rand, requires_grad=False) + elif self.pnet_type == "alex": + self.net = alexnet(pretrained=not self.pnet_rand, requires_grad=False) + elif self.pnet_type[:-2] == "resnet": + self.net = resnet( + pretrained=not self.pnet_rand, + requires_grad=False, + num=int(self.pnet_type[-2:]), + ) + elif self.pnet_type == "squeeze": + self.net = squeezenet(pretrained=not self.pnet_rand, requires_grad=False) + + self.L = self.net.N_slices + + if use_gpu: + self.net.cuda() + self.shift = self.shift.cuda() + self.scale = self.scale.cuda() + + def forward(self, in0, in1, retPerLayer=False): + in0_sc = (in0 - self.shift.expand_as(in0)) / self.scale.expand_as(in0) + in1_sc = (in1 - self.shift.expand_as(in0)) / self.scale.expand_as(in0) + + outs0 = self.net.forward(in0_sc) + outs1 = self.net.forward(in1_sc) + + if retPerLayer: + all_scores = [] + for kk, out0 in enumerate(outs0): + cur_score = 1.0 - cos_sim(outs0[kk], outs1[kk]) + if kk == 0: + val = 1.0 * cur_score + else: + val = val + cur_score + if retPerLayer: + all_scores += [cur_score] + + if retPerLayer: + return (val, all_scores) + else: + return val + + +# The SSIM metric +def ssim_metric(img1, img2, mask=None): + return ssim(img1, img2, mask=mask, size_average=False) + + +# The PSNR metric +def psnr(img1, img2, mask=None, reshape=False): + b = img1.size(0) + if not (mask is None): + b = img1.size(0) + mse_err = (img1 - img2).pow(2) * mask + if reshape: + mse_err = mse_err.reshape(b, -1).sum(dim=1) / ( + 3 * mask.reshape(b, -1).sum(dim=1).clamp(min=1) + ) + else: + mse_err = mse_err.view(b, -1).sum(dim=1) / ( + 3 * mask.view(b, -1).sum(dim=1).clamp(min=1) + ) + else: + if reshape: + mse_err = (img1 - img2).pow(2).reshape(b, -1).mean(dim=1) + else: + mse_err = (img1 - img2).pow(2).view(b, -1).mean(dim=1) + + psnr = 10 * (1 / mse_err).log10() + return psnr + + +# The perceptual similarity metric +def perceptual_sim(img1, img2, vgg16): + # First extract features + dist = vgg16(img1 * 2 - 1, img2 * 2 - 1) + + return dist + + +def load_img(img_name, size=None): + try: + img = Image.open(img_name) + + if type(size) == int: + img = img.resize((size, size)) + elif size is not None: + img = img.resize((size[1], size[0])) + + img = transform(img).cuda() + img = img.unsqueeze(0) + except Exception as e: + print("Failed at loading %s " % img_name) + print(e) + img = torch.zeros(1, 3, 256, 256).cuda() + raise + return img + + +def compute_perceptual_similarity(folder, pred_img, tgt_img, take_every_other): + # Load VGG16 for feature similarity + vgg16 = PNet().to("cuda") + vgg16.eval() + vgg16.cuda() + + values_percsim = [] + values_ssim = [] + values_psnr = [] + folders = os.listdir(folder) + for i, f in tqdm(enumerate(sorted(folders))): + pred_imgs = glob.glob(folder + f + "/" + pred_img) + tgt_imgs = glob.glob(folder + f + "/" + tgt_img) + assert len(tgt_imgs) == 1 + + perc_sim = 10000 + ssim_sim = -10 + psnr_sim = -10 + for p_img in pred_imgs: + t_img = load_img(tgt_imgs[0]) + p_img = load_img(p_img, size=t_img.shape[2:]) + t_perc_sim = perceptual_sim(p_img, t_img, vgg16).item() + perc_sim = min(perc_sim, t_perc_sim) + + ssim_sim = max(ssim_sim, ssim_metric(p_img, t_img).item()) + psnr_sim = max(psnr_sim, psnr(p_img, t_img).item()) + + values_percsim += [perc_sim] + values_ssim += [ssim_sim] + values_psnr += [psnr_sim] + + if take_every_other: + n_valuespercsim = [] + n_valuesssim = [] + n_valuespsnr = [] + for i in range(0, len(values_percsim) // 2): + n_valuespercsim += [min(values_percsim[2 * i], values_percsim[2 * i + 1])] + n_valuespsnr += [max(values_psnr[2 * i], values_psnr[2 * i + 1])] + n_valuesssim += [max(values_ssim[2 * i], values_ssim[2 * i + 1])] + + values_percsim = n_valuespercsim + values_ssim = n_valuesssim + values_psnr = n_valuespsnr + + avg_percsim = np.mean(np.array(values_percsim)) + std_percsim = np.std(np.array(values_percsim)) + + avg_psnr = np.mean(np.array(values_psnr)) + std_psnr = np.std(np.array(values_psnr)) + + avg_ssim = np.mean(np.array(values_ssim)) + std_ssim = np.std(np.array(values_ssim)) + + return { + "Perceptual similarity": (avg_percsim, std_percsim), + "PSNR": (avg_psnr, std_psnr), + "SSIM": (avg_ssim, std_ssim), + } + + +def compute_perceptual_similarity_from_list( + pred_imgs_list, tgt_imgs_list, take_every_other, simple_format=True +): + # Load VGG16 for feature similarity + vgg16 = PNet().to("cuda") + vgg16.eval() + vgg16.cuda() + + values_percsim = [] + values_ssim = [] + values_psnr = [] + equal_count = 0 + ambig_count = 0 + for i, tgt_img in enumerate(tqdm(tgt_imgs_list)): + pred_imgs = pred_imgs_list[i] + tgt_imgs = [tgt_img] + assert len(tgt_imgs) == 1 + + if type(pred_imgs) != list: + pred_imgs = [pred_imgs] + + perc_sim = 10000 + ssim_sim = -10 + psnr_sim = -10 + assert len(pred_imgs) > 0 + for p_img in pred_imgs: + t_img = load_img(tgt_imgs[0]) + p_img = load_img(p_img, size=t_img.shape[2:]) + t_perc_sim = perceptual_sim(p_img, t_img, vgg16).item() + perc_sim = min(perc_sim, t_perc_sim) + + ssim_sim = max(ssim_sim, ssim_metric(p_img, t_img).item()) + psnr_sim = max(psnr_sim, psnr(p_img, t_img).item()) + + values_percsim += [perc_sim] + values_ssim += [ssim_sim] + if psnr_sim != np.float("inf"): + values_psnr += [psnr_sim] + else: + if torch.allclose(p_img, t_img): + equal_count += 1 + print("{} equal src and wrp images.".format(equal_count)) + else: + ambig_count += 1 + print("{} ambiguous src and wrp images.".format(ambig_count)) + + if take_every_other: + n_valuespercsim = [] + n_valuesssim = [] + n_valuespsnr = [] + for i in range(0, len(values_percsim) // 2): + n_valuespercsim += [min(values_percsim[2 * i], values_percsim[2 * i + 1])] + n_valuespsnr += [max(values_psnr[2 * i], values_psnr[2 * i + 1])] + n_valuesssim += [max(values_ssim[2 * i], values_ssim[2 * i + 1])] + + values_percsim = n_valuespercsim + values_ssim = n_valuesssim + values_psnr = n_valuespsnr + + avg_percsim = np.mean(np.array(values_percsim)) + std_percsim = np.std(np.array(values_percsim)) + + avg_psnr = np.mean(np.array(values_psnr)) + std_psnr = np.std(np.array(values_psnr)) + + avg_ssim = np.mean(np.array(values_ssim)) + std_ssim = np.std(np.array(values_ssim)) + + if simple_format: + # just to make yaml formatting readable + return { + "Perceptual similarity": [float(avg_percsim), float(std_percsim)], + "PSNR": [float(avg_psnr), float(std_psnr)], + "SSIM": [float(avg_ssim), float(std_ssim)], + } + else: + return { + "Perceptual similarity": (avg_percsim, std_percsim), + "PSNR": (avg_psnr, std_psnr), + "SSIM": (avg_ssim, std_ssim), + } + + +def compute_perceptual_similarity_from_list_topk( + pred_imgs_list, tgt_imgs_list, take_every_other, resize=False +): + # Load VGG16 for feature similarity + vgg16 = PNet().to("cuda") + vgg16.eval() + vgg16.cuda() + + values_percsim = [] + values_ssim = [] + values_psnr = [] + individual_percsim = [] + individual_ssim = [] + individual_psnr = [] + for i, tgt_img in enumerate(tqdm(tgt_imgs_list)): + pred_imgs = pred_imgs_list[i] + tgt_imgs = [tgt_img] + assert len(tgt_imgs) == 1 + + if type(pred_imgs) != list: + assert False + pred_imgs = [pred_imgs] + + perc_sim = 10000 + ssim_sim = -10 + psnr_sim = -10 + sample_percsim = list() + sample_ssim = list() + sample_psnr = list() + for p_img in pred_imgs: + if resize: + t_img = load_img(tgt_imgs[0], size=(256, 256)) + else: + t_img = load_img(tgt_imgs[0]) + p_img = load_img(p_img, size=t_img.shape[2:]) + + t_perc_sim = perceptual_sim(p_img, t_img, vgg16).item() + sample_percsim.append(t_perc_sim) + perc_sim = min(perc_sim, t_perc_sim) + + t_ssim = ssim_metric(p_img, t_img).item() + sample_ssim.append(t_ssim) + ssim_sim = max(ssim_sim, t_ssim) + + t_psnr = psnr(p_img, t_img).item() + sample_psnr.append(t_psnr) + psnr_sim = max(psnr_sim, t_psnr) + + values_percsim += [perc_sim] + values_ssim += [ssim_sim] + values_psnr += [psnr_sim] + individual_percsim.append(sample_percsim) + individual_ssim.append(sample_ssim) + individual_psnr.append(sample_psnr) + + if take_every_other: + assert False, "Do this later, after specifying topk to get proper results" + n_valuespercsim = [] + n_valuesssim = [] + n_valuespsnr = [] + for i in range(0, len(values_percsim) // 2): + n_valuespercsim += [min(values_percsim[2 * i], values_percsim[2 * i + 1])] + n_valuespsnr += [max(values_psnr[2 * i], values_psnr[2 * i + 1])] + n_valuesssim += [max(values_ssim[2 * i], values_ssim[2 * i + 1])] + + values_percsim = n_valuespercsim + values_ssim = n_valuesssim + values_psnr = n_valuespsnr + + avg_percsim = np.mean(np.array(values_percsim)) + std_percsim = np.std(np.array(values_percsim)) + + avg_psnr = np.mean(np.array(values_psnr)) + std_psnr = np.std(np.array(values_psnr)) + + avg_ssim = np.mean(np.array(values_ssim)) + std_ssim = np.std(np.array(values_ssim)) + + individual_percsim = np.array(individual_percsim) + individual_psnr = np.array(individual_psnr) + individual_ssim = np.array(individual_ssim) + + return { + "avg_of_best": { + "Perceptual similarity": [float(avg_percsim), float(std_percsim)], + "PSNR": [float(avg_psnr), float(std_psnr)], + "SSIM": [float(avg_ssim), float(std_ssim)], + }, + "individual": { + "PSIM": individual_percsim, + "PSNR": individual_psnr, + "SSIM": individual_ssim, + }, + } + + +if __name__ == "__main__": + args = argparse.ArgumentParser() + args.add_argument("--folder", type=str, default="") + args.add_argument("--pred_image", type=str, default="") + args.add_argument("--target_image", type=str, default="") + args.add_argument("--take_every_other", action="store_true", default=False) + args.add_argument("--output_file", type=str, default="") + + opts = args.parse_args() + + folder = opts.folder + pred_img = opts.pred_image + tgt_img = opts.target_image + + results = compute_perceptual_similarity( + folder, pred_img, tgt_img, opts.take_every_other + ) + + f = open(opts.output_file, "w") + for key in results: + print("%s for %s: \n" % (key, opts.folder)) + print("\t {:0.4f} | {:0.4f} \n".format(results[key][0], results[key][1])) + + f.write("%s for %s: \n" % (key, opts.folder)) + f.write("\t {:0.4f} | {:0.4f} \n".format(results[key][0], results[key][1])) + + f.close() diff --git a/extern/ldm_zero123/modules/evaluate/frechet_video_distance.py b/extern/ldm_zero123/modules/evaluate/frechet_video_distance.py new file mode 100755 index 0000000..61688d0 --- /dev/null +++ b/extern/ldm_zero123/modules/evaluate/frechet_video_distance.py @@ -0,0 +1,147 @@ +# coding=utf-8 +# Copyright 2022 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Lint as: python2, python3 +"""Minimal Reference implementation for the Frechet Video Distance (FVD). + +FVD is a metric for the quality of video generation models. It is inspired by +the FID (Frechet Inception Distance) used for images, but uses a different +embedding to be better suitable for videos. +""" + +from __future__ import absolute_import, division, print_function + +import six +import tensorflow.compat.v1 as tf +import tensorflow_gan as tfgan +import tensorflow_hub as hub + + +def preprocess(videos, target_resolution): + """Runs some preprocessing on the videos for I3D model. + + Args: + videos: [batch_size, num_frames, height, width, depth] The videos to be + preprocessed. We don't care about the specific dtype of the videos, it can + be anything that tf.image.resize_bilinear accepts. Values are expected to + be in the range 0-255. + target_resolution: (width, height): target video resolution + + Returns: + videos: [batch_size, num_frames, height, width, depth] + """ + videos_shape = list(videos.shape) + all_frames = tf.reshape(videos, [-1] + videos_shape[-3:]) + resized_videos = tf.image.resize_bilinear(all_frames, size=target_resolution) + target_shape = [videos_shape[0], -1] + list(target_resolution) + [3] + output_videos = tf.reshape(resized_videos, target_shape) + scaled_videos = 2.0 * tf.cast(output_videos, tf.float32) / 255.0 - 1 + return scaled_videos + + +def _is_in_graph(tensor_name): + """Checks whether a given tensor does exists in the graph.""" + try: + tf.get_default_graph().get_tensor_by_name(tensor_name) + except KeyError: + return False + return True + + +def create_id3_embedding(videos, warmup=False, batch_size=16): + """Embeds the given videos using the Inflated 3D Convolution ne twork. + + Downloads the graph of the I3D from tf.hub and adds it to the graph on the + first call. + + Args: + videos: [batch_size, num_frames, height=224, width=224, depth=3]. + Expected range is [-1, 1]. + + Returns: + embedding: [batch_size, embedding_size]. embedding_size depends + on the model used. + + Raises: + ValueError: when a provided embedding_layer is not supported. + """ + + # batch_size = 16 + module_spec = "https://tfhub.dev/deepmind/i3d-kinetics-400/1" + + # Making sure that we import the graph separately for + # each different input video tensor. + module_name = "fvd_kinetics-400_id3_module_" + six.ensure_str(videos.name).replace( + ":", "_" + ) + + assert_ops = [ + tf.Assert( + tf.reduce_max(videos) <= 1.001, ["max value in frame is > 1", videos] + ), + tf.Assert( + tf.reduce_min(videos) >= -1.001, ["min value in frame is < -1", videos] + ), + tf.assert_equal( + tf.shape(videos)[0], + batch_size, + ["invalid frame batch size: ", tf.shape(videos)], + summarize=6, + ), + ] + with tf.control_dependencies(assert_ops): + videos = tf.identity(videos) + + module_scope = "%s_apply_default/" % module_name + + # To check whether the module has already been loaded into the graph, we look + # for a given tensor name. If this tensor name exists, we assume the function + # has been called before and the graph was imported. Otherwise we import it. + # Note: in theory, the tensor could exist, but have wrong shapes. + # This will happen if create_id3_embedding is called with a frames_placehoder + # of wrong size/batch size, because even though that will throw a tf.Assert + # on graph-execution time, it will insert the tensor (with wrong shape) into + # the graph. This is why we need the following assert. + if warmup: + video_batch_size = int(videos.shape[0]) + assert video_batch_size in [ + batch_size, + -1, + None, + ], f"Invalid batch size {video_batch_size}" + tensor_name = module_scope + "RGB/inception_i3d/Mean:0" + if not _is_in_graph(tensor_name): + i3d_model = hub.Module(module_spec, name=module_name) + i3d_model(videos) + + # gets the kinetics-i3d-400-logits layer + tensor_name = module_scope + "RGB/inception_i3d/Mean:0" + tensor = tf.get_default_graph().get_tensor_by_name(tensor_name) + return tensor + + +def calculate_fvd(real_activations, generated_activations): + """Returns a list of ops that compute metrics as funcs of activations. + + Args: + real_activations: [num_samples, embedding_size] + generated_activations: [num_samples, embedding_size] + + Returns: + A scalar that contains the requested FVD. + """ + return tfgan.eval.frechet_classifier_distance_from_activations( + real_activations, generated_activations + ) diff --git a/extern/ldm_zero123/modules/evaluate/ssim.py b/extern/ldm_zero123/modules/evaluate/ssim.py new file mode 100755 index 0000000..b640df0 --- /dev/null +++ b/extern/ldm_zero123/modules/evaluate/ssim.py @@ -0,0 +1,118 @@ +# MIT Licence + +# Methods to predict the SSIM, taken from +# https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py + +from math import exp + +import torch +import torch.nn.functional as F +from torch.autograd import Variable + + +def gaussian(window_size, sigma): + gauss = torch.Tensor( + [ + exp(-((x - window_size // 2) ** 2) / float(2 * sigma**2)) + for x in range(window_size) + ] + ) + return gauss / gauss.sum() + + +def create_window(window_size, channel): + _1D_window = gaussian(window_size, 1.5).unsqueeze(1) + _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) + window = Variable( + _2D_window.expand(channel, 1, window_size, window_size).contiguous() + ) + return window + + +def _ssim(img1, img2, window, window_size, channel, mask=None, size_average=True): + mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) + mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) + + mu1_sq = mu1.pow(2) + mu2_sq = mu2.pow(2) + mu1_mu2 = mu1 * mu2 + + sigma1_sq = ( + F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq + ) + sigma2_sq = ( + F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq + ) + sigma12 = ( + F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) + - mu1_mu2 + ) + + C1 = (0.01) ** 2 + C2 = (0.03) ** 2 + + ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ( + (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2) + ) + + if not (mask is None): + b = mask.size(0) + ssim_map = ssim_map.mean(dim=1, keepdim=True) * mask + ssim_map = ssim_map.view(b, -1).sum(dim=1) / mask.view(b, -1).sum(dim=1).clamp( + min=1 + ) + return ssim_map + + import pdb + + pdb.set_trace + + if size_average: + return ssim_map.mean() + else: + return ssim_map.mean(1).mean(1).mean(1) + + +class SSIM(torch.nn.Module): + def __init__(self, window_size=11, size_average=True): + super(SSIM, self).__init__() + self.window_size = window_size + self.size_average = size_average + self.channel = 1 + self.window = create_window(window_size, self.channel) + + def forward(self, img1, img2, mask=None): + (_, channel, _, _) = img1.size() + + if channel == self.channel and self.window.data.type() == img1.data.type(): + window = self.window + else: + window = create_window(self.window_size, channel) + + if img1.is_cuda: + window = window.cuda(img1.get_device()) + window = window.type_as(img1) + + self.window = window + self.channel = channel + + return _ssim( + img1, + img2, + window, + self.window_size, + channel, + mask, + self.size_average, + ) + + +def ssim(img1, img2, window_size=11, mask=None, size_average=True): + (_, channel, _, _) = img1.size() + window = create_window(window_size, channel) + + if img1.is_cuda: + window = window.cuda(img1.get_device()) + window = window.type_as(img1) + + return _ssim(img1, img2, window, window_size, channel, mask, size_average) diff --git a/extern/ldm_zero123/modules/evaluate/torch_frechet_video_distance.py b/extern/ldm_zero123/modules/evaluate/torch_frechet_video_distance.py new file mode 100755 index 0000000..c4cd40f --- /dev/null +++ b/extern/ldm_zero123/modules/evaluate/torch_frechet_video_distance.py @@ -0,0 +1,331 @@ +# based on https://github.com/universome/fvd-comparison/blob/master/compare_models.py; huge thanks! +import glob +import hashlib +import html +import io +import multiprocessing as mp +import os +import re +import urllib +import urllib.request +from typing import Any, Callable, Dict, List, Tuple, Union + +import numpy as np +import requests +import scipy.linalg +import torch +from torchvision.io import read_video +from tqdm import tqdm + +torch.set_grad_enabled(False) +from einops import rearrange +from nitro.util import isvideo + + +def compute_frechet_distance(mu_sample, sigma_sample, mu_ref, sigma_ref) -> float: + print("Calculate frechet distance...") + m = np.square(mu_sample - mu_ref).sum() + s, _ = scipy.linalg.sqrtm( + np.dot(sigma_sample, sigma_ref), disp=False + ) # pylint: disable=no-member + fid = np.real(m + np.trace(sigma_sample + sigma_ref - s * 2)) + + return float(fid) + + +def compute_stats(feats: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + mu = feats.mean(axis=0) # [d] + sigma = np.cov(feats, rowvar=False) # [d, d] + + return mu, sigma + + +def open_url( + url: str, + num_attempts: int = 10, + verbose: bool = True, + return_filename: bool = False, +) -> Any: + """Download the given URL and return a binary-mode file object to access the data.""" + assert num_attempts >= 1 + + # Doesn't look like an URL scheme so interpret it as a local filename. + if not re.match("^[a-z]+://", url): + return url if return_filename else open(url, "rb") + + # Handle file URLs. This code handles unusual file:// patterns that + # arise on Windows: + # + # file:///c:/foo.txt + # + # which would translate to a local '/c:/foo.txt' filename that's + # invalid. Drop the forward slash for such pathnames. + # + # If you touch this code path, you should test it on both Linux and + # Windows. + # + # Some internet resources suggest using urllib.request.url2pathname() but + # but that converts forward slashes to backslashes and this causes + # its own set of problems. + if url.startswith("file://"): + filename = urllib.parse.urlparse(url).path + if re.match(r"^/[a-zA-Z]:", filename): + filename = filename[1:] + return filename if return_filename else open(filename, "rb") + + url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest() + + # Download. + url_name = None + url_data = None + with requests.Session() as session: + if verbose: + print("Downloading %s ..." % url, end="", flush=True) + for attempts_left in reversed(range(num_attempts)): + try: + with session.get(url) as res: + res.raise_for_status() + if len(res.content) == 0: + raise IOError("No data received") + + if len(res.content) < 8192: + content_str = res.content.decode("utf-8") + if "download_warning" in res.headers.get("Set-Cookie", ""): + links = [ + html.unescape(link) + for link in content_str.split('"') + if "export=download" in link + ] + if len(links) == 1: + url = requests.compat.urljoin(url, links[0]) + raise IOError("Google Drive virus checker nag") + if "Google Drive - Quota exceeded" in content_str: + raise IOError( + "Google Drive download quota exceeded -- please try again later" + ) + + match = re.search( + r'filename="([^"]*)"', + res.headers.get("Content-Disposition", ""), + ) + url_name = match[1] if match else url + url_data = res.content + if verbose: + print(" done") + break + except KeyboardInterrupt: + raise + except: + if not attempts_left: + if verbose: + print(" failed") + raise + if verbose: + print(".", end="", flush=True) + + # Return data as file object. + assert not return_filename + return io.BytesIO(url_data) + + +def load_video(ip): + vid, *_ = read_video(ip) + vid = rearrange(vid, "t h w c -> t c h w").to(torch.uint8) + return vid + + +def get_data_from_str(input_str, nprc=None): + assert os.path.isdir( + input_str + ), f'Specified input folder "{input_str}" is not a directory' + vid_filelist = glob.glob(os.path.join(input_str, "*.mp4")) + print(f"Found {len(vid_filelist)} videos in dir {input_str}") + + if nprc is None: + try: + nprc = mp.cpu_count() + except NotImplementedError: + print( + "WARNING: cpu_count() not avlailable, using only 1 cpu for video loading" + ) + nprc = 1 + + pool = mp.Pool(processes=nprc) + + vids = [] + for v in tqdm( + pool.imap_unordered(load_video, vid_filelist), + total=len(vid_filelist), + desc="Loading videos...", + ): + vids.append(v) + + vids = torch.stack(vids, dim=0).float() + + return vids + + +def get_stats(stats): + assert os.path.isfile(stats) and stats.endswith( + ".npz" + ), f"no stats found under {stats}" + + print(f"Using precomputed statistics under {stats}") + stats = np.load(stats) + stats = {key: stats[key] for key in stats.files} + + return stats + + +@torch.no_grad() +def compute_fvd( + ref_input, sample_input, bs=32, ref_stats=None, sample_stats=None, nprc_load=None +): + calc_stats = ref_stats is None or sample_stats is None + + if calc_stats: + only_ref = sample_stats is not None + only_sample = ref_stats is not None + + if isinstance(ref_input, str) and not only_sample: + ref_input = get_data_from_str(ref_input, nprc_load) + + if isinstance(sample_input, str) and not only_ref: + sample_input = get_data_from_str(sample_input, nprc_load) + + stats = compute_statistics( + sample_input, + ref_input, + device="cuda" if torch.cuda.is_available() else "cpu", + bs=bs, + only_ref=only_ref, + only_sample=only_sample, + ) + + if only_ref: + stats.update(get_stats(sample_stats)) + elif only_sample: + stats.update(get_stats(ref_stats)) + + else: + stats = get_stats(sample_stats) + stats.update(get_stats(ref_stats)) + + fvd = compute_frechet_distance(**stats) + + return { + "FVD": fvd, + } + + +@torch.no_grad() +def compute_statistics( + videos_fake, + videos_real, + device: str = "cuda", + bs=32, + only_ref=False, + only_sample=False, +) -> Dict: + detector_url = "https://www.dropbox.com/s/ge9e5ujwgetktms/i3d_torchscript.pt?dl=1" + detector_kwargs = dict( + rescale=True, resize=True, return_features=True + ) # Return raw features before the softmax layer. + + with open_url(detector_url, verbose=False) as f: + detector = torch.jit.load(f).eval().to(device) + + assert not ( + only_sample and only_ref + ), "only_ref and only_sample arguments are mutually exclusive" + + ref_embed, sample_embed = [], [] + + info = f"Computing I3D activations for FVD score with batch size {bs}" + + if only_ref: + if not isvideo(videos_real): + # if not is video we assume to have numpy arrays pf shape (n_vids, t, h, w, c) in range [0,255] + videos_real = torch.from_numpy(videos_real).permute(0, 4, 1, 2, 3).float() + print(videos_real.shape) + + if videos_real.shape[0] % bs == 0: + n_secs = videos_real.shape[0] // bs + else: + n_secs = videos_real.shape[0] // bs + 1 + + videos_real = torch.tensor_split(videos_real, n_secs, dim=0) + + for ref_v in tqdm(videos_real, total=len(videos_real), desc=info): + feats_ref = ( + detector(ref_v.to(device).contiguous(), **detector_kwargs).cpu().numpy() + ) + ref_embed.append(feats_ref) + + elif only_sample: + if not isvideo(videos_fake): + # if not is video we assume to have numpy arrays pf shape (n_vids, t, h, w, c) in range [0,255] + videos_fake = torch.from_numpy(videos_fake).permute(0, 4, 1, 2, 3).float() + print(videos_fake.shape) + + if videos_fake.shape[0] % bs == 0: + n_secs = videos_fake.shape[0] // bs + else: + n_secs = videos_fake.shape[0] // bs + 1 + + videos_real = torch.tensor_split(videos_real, n_secs, dim=0) + + for sample_v in tqdm(videos_fake, total=len(videos_real), desc=info): + feats_sample = ( + detector(sample_v.to(device).contiguous(), **detector_kwargs) + .cpu() + .numpy() + ) + sample_embed.append(feats_sample) + + else: + if not isvideo(videos_real): + # if not is video we assume to have numpy arrays pf shape (n_vids, t, h, w, c) in range [0,255] + videos_real = torch.from_numpy(videos_real).permute(0, 4, 1, 2, 3).float() + + if not isvideo(videos_fake): + videos_fake = torch.from_numpy(videos_fake).permute(0, 4, 1, 2, 3).float() + + if videos_fake.shape[0] % bs == 0: + n_secs = videos_fake.shape[0] // bs + else: + n_secs = videos_fake.shape[0] // bs + 1 + + videos_real = torch.tensor_split(videos_real, n_secs, dim=0) + videos_fake = torch.tensor_split(videos_fake, n_secs, dim=0) + + for ref_v, sample_v in tqdm( + zip(videos_real, videos_fake), total=len(videos_fake), desc=info + ): + # print(ref_v.shape) + # ref_v = torch.nn.functional.interpolate(ref_v, size=(sample_v.shape[2], 256, 256), mode='trilinear', align_corners=False) + # sample_v = torch.nn.functional.interpolate(sample_v, size=(sample_v.shape[2], 256, 256), mode='trilinear', align_corners=False) + + feats_sample = ( + detector(sample_v.to(device).contiguous(), **detector_kwargs) + .cpu() + .numpy() + ) + feats_ref = ( + detector(ref_v.to(device).contiguous(), **detector_kwargs).cpu().numpy() + ) + sample_embed.append(feats_sample) + ref_embed.append(feats_ref) + + out = dict() + if len(sample_embed) > 0: + sample_embed = np.concatenate(sample_embed, axis=0) + mu_sample, sigma_sample = compute_stats(sample_embed) + out.update({"mu_sample": mu_sample, "sigma_sample": sigma_sample}) + + if len(ref_embed) > 0: + ref_embed = np.concatenate(ref_embed, axis=0) + mu_ref, sigma_ref = compute_stats(ref_embed) + out.update({"mu_ref": mu_ref, "sigma_ref": sigma_ref}) + + return out diff --git a/extern/ldm_zero123/modules/image_degradation/__init__.py b/extern/ldm_zero123/modules/image_degradation/__init__.py new file mode 100755 index 0000000..1143c44 --- /dev/null +++ b/extern/ldm_zero123/modules/image_degradation/__init__.py @@ -0,0 +1,6 @@ +from extern.ldm_zero123.modules.image_degradation.bsrgan import ( + degradation_bsrgan_variant as degradation_fn_bsr, +) +from extern.ldm_zero123.modules.image_degradation.bsrgan_light import ( + degradation_bsrgan_variant as degradation_fn_bsr_light, +) diff --git a/extern/ldm_zero123/modules/image_degradation/bsrgan.py b/extern/ldm_zero123/modules/image_degradation/bsrgan.py new file mode 100755 index 0000000..3b2e534 --- /dev/null +++ b/extern/ldm_zero123/modules/image_degradation/bsrgan.py @@ -0,0 +1,809 @@ +# -*- coding: utf-8 -*- +""" +# -------------------------------------------- +# Super-Resolution +# -------------------------------------------- +# +# Kai Zhang (cskaizhang@gmail.com) +# https://github.com/cszn +# From 2019/03--2021/08 +# -------------------------------------------- +""" + +import random +from functools import partial + +import albumentations +import cv2 +import numpy as np +import scipy +import scipy.stats as ss +import torch +from scipy import ndimage +from scipy.interpolate import interp2d +from scipy.linalg import orth + +import extern.ldm_zero123.modules.image_degradation.utils_image as util + + +def modcrop_np(img, sf): + """ + Args: + img: numpy image, WxH or WxHxC + sf: scale factor + Return: + cropped image + """ + w, h = img.shape[:2] + im = np.copy(img) + return im[: w - w % sf, : h - h % sf, ...] + + +""" +# -------------------------------------------- +# anisotropic Gaussian kernels +# -------------------------------------------- +""" + + +def analytic_kernel(k): + """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" + k_size = k.shape[0] + # Calculate the big kernels size + big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) + # Loop over the small kernel to fill the big one + for r in range(k_size): + for c in range(k_size): + big_k[2 * r : 2 * r + k_size, 2 * c : 2 * c + k_size] += k[r, c] * k + # Crop the edges of the big kernel to ignore very small values and increase run time of SR + crop = k_size // 2 + cropped_big_k = big_k[crop:-crop, crop:-crop] + # Normalize to 1 + return cropped_big_k / cropped_big_k.sum() + + +def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): + """generate an anisotropic Gaussian kernel + Args: + ksize : e.g., 15, kernel size + theta : [0, pi], rotation angle range + l1 : [0.1,50], scaling of eigenvalues + l2 : [0.1,l1], scaling of eigenvalues + If l1 = l2, will get an isotropic Gaussian kernel. + Returns: + k : kernel + """ + + v = np.dot( + np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), + np.array([1.0, 0.0]), + ) + V = np.array([[v[0], v[1]], [v[1], -v[0]]]) + D = np.array([[l1, 0], [0, l2]]) + Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) + k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) + + return k + + +def gm_blur_kernel(mean, cov, size=15): + center = size / 2.0 + 0.5 + k = np.zeros([size, size]) + for y in range(size): + for x in range(size): + cy = y - center + 1 + cx = x - center + 1 + k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) + + k = k / np.sum(k) + return k + + +def shift_pixel(x, sf, upper_left=True): + """shift pixel for super-resolution with different scale factors + Args: + x: WxHxC or WxH + sf: scale factor + upper_left: shift direction + """ + h, w = x.shape[:2] + shift = (sf - 1) * 0.5 + xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) + if upper_left: + x1 = xv + shift + y1 = yv + shift + else: + x1 = xv - shift + y1 = yv - shift + + x1 = np.clip(x1, 0, w - 1) + y1 = np.clip(y1, 0, h - 1) + + if x.ndim == 2: + x = interp2d(xv, yv, x)(x1, y1) + if x.ndim == 3: + for i in range(x.shape[-1]): + x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) + + return x + + +def blur(x, k): + """ + x: image, NxcxHxW + k: kernel, Nx1xhxw + """ + n, c = x.shape[:2] + p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 + x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode="replicate") + k = k.repeat(1, c, 1, 1) + k = k.view(-1, 1, k.shape[2], k.shape[3]) + x = x.view(1, -1, x.shape[2], x.shape[3]) + x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) + x = x.view(n, c, x.shape[2], x.shape[3]) + + return x + + +def gen_kernel( + k_size=np.array([15, 15]), + scale_factor=np.array([4, 4]), + min_var=0.6, + max_var=10.0, + noise_level=0, +): + """ " + # modified version of https://github.com/assafshocher/BlindSR_dataset_generator + # Kai Zhang + # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var + # max_var = 2.5 * sf + """ + # Set random eigen-vals (lambdas) and angle (theta) for COV matrix + lambda_1 = min_var + np.random.rand() * (max_var - min_var) + lambda_2 = min_var + np.random.rand() * (max_var - min_var) + theta = np.random.rand() * np.pi # random theta + noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 + + # Set COV matrix using Lambdas and Theta + LAMBDA = np.diag([lambda_1, lambda_2]) + Q = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) + SIGMA = Q @ LAMBDA @ Q.T + INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] + + # Set expectation position (shifting kernel for aligned image) + MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) + MU = MU[None, None, :, None] + + # Create meshgrid for Gaussian + [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) + Z = np.stack([X, Y], 2)[:, :, :, None] + + # Calcualte Gaussian for every pixel of the kernel + ZZ = Z - MU + ZZ_t = ZZ.transpose(0, 1, 3, 2) + raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) + + # shift the kernel so it will be centered + # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) + + # Normalize the kernel and return + # kernel = raw_kernel_centered / np.sum(raw_kernel_centered) + kernel = raw_kernel / np.sum(raw_kernel) + return kernel + + +def fspecial_gaussian(hsize, sigma): + hsize = [hsize, hsize] + siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] + std = sigma + [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) + arg = -(x * x + y * y) / (2 * std * std) + h = np.exp(arg) + h[h < scipy.finfo(float).eps * h.max()] = 0 + sumh = h.sum() + if sumh != 0: + h = h / sumh + return h + + +def fspecial_laplacian(alpha): + alpha = max([0, min([alpha, 1])]) + h1 = alpha / (alpha + 1) + h2 = (1 - alpha) / (alpha + 1) + h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] + h = np.array(h) + return h + + +def fspecial(filter_type, *args, **kwargs): + """ + python code from: + https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py + """ + if filter_type == "gaussian": + return fspecial_gaussian(*args, **kwargs) + if filter_type == "laplacian": + return fspecial_laplacian(*args, **kwargs) + + +""" +# -------------------------------------------- +# degradation models +# -------------------------------------------- +""" + + +def bicubic_degradation(x, sf=3): + """ + Args: + x: HxWxC image, [0, 1] + sf: down-scale factor + Return: + bicubicly downsampled LR image + """ + x = util.imresize_np(x, scale=1 / sf) + return x + + +def srmd_degradation(x, k, sf=3): + """blur + bicubic downsampling + Args: + x: HxWxC image, [0, 1] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + Reference: + @inproceedings{zhang2018learning, + title={Learning a single convolutional super-resolution network for multiple degradations}, + author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + pages={3262--3271}, + year={2018} + } + """ + x = ndimage.filters.convolve( + x, np.expand_dims(k, axis=2), mode="wrap" + ) # 'nearest' | 'mirror' + x = bicubic_degradation(x, sf=sf) + return x + + +def dpsr_degradation(x, k, sf=3): + """bicubic downsampling + blur + Args: + x: HxWxC image, [0, 1] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + Reference: + @inproceedings{zhang2019deep, + title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, + author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + pages={1671--1681}, + year={2019} + } + """ + x = bicubic_degradation(x, sf=sf) + x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode="wrap") + return x + + +def classical_degradation(x, k, sf=3): + """blur + downsampling + Args: + x: HxWxC image, [0, 1]/[0, 255] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + """ + x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode="wrap") + # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) + st = 0 + return x[st::sf, st::sf, ...] + + +def add_sharpening(img, weight=0.5, radius=50, threshold=10): + """USM sharpening. borrowed from real-ESRGAN + Input image: I; Blurry image: B. + 1. K = I + weight * (I - B) + 2. Mask = 1 if abs(I - B) > threshold, else: 0 + 3. Blur mask: + 4. Out = Mask * K + (1 - Mask) * I + Args: + img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. + weight (float): Sharp weight. Default: 1. + radius (float): Kernel size of Gaussian blur. Default: 50. + threshold (int): + """ + if radius % 2 == 0: + radius += 1 + blur = cv2.GaussianBlur(img, (radius, radius), 0) + residual = img - blur + mask = np.abs(residual) * 255 > threshold + mask = mask.astype("float32") + soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) + + K = img + weight * residual + K = np.clip(K, 0, 1) + return soft_mask * K + (1 - soft_mask) * img + + +def add_blur(img, sf=4): + wd2 = 4.0 + sf + wd = 2.0 + 0.2 * sf + if random.random() < 0.5: + l1 = wd2 * random.random() + l2 = wd2 * random.random() + k = anisotropic_Gaussian( + ksize=2 * random.randint(2, 11) + 3, + theta=random.random() * np.pi, + l1=l1, + l2=l2, + ) + else: + k = fspecial("gaussian", 2 * random.randint(2, 11) + 3, wd * random.random()) + img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode="mirror") + + return img + + +def add_resize(img, sf=4): + rnum = np.random.rand() + if rnum > 0.8: # up + sf1 = random.uniform(1, 2) + elif rnum < 0.7: # down + sf1 = random.uniform(0.5 / sf, 1) + else: + sf1 = 1.0 + img = cv2.resize( + img, + (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), + interpolation=random.choice([1, 2, 3]), + ) + img = np.clip(img, 0.0, 1.0) + + return img + + +# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): +# noise_level = random.randint(noise_level1, noise_level2) +# rnum = np.random.rand() +# if rnum > 0.6: # add color Gaussian noise +# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) +# elif rnum < 0.4: # add grayscale Gaussian noise +# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) +# else: # add noise +# L = noise_level2 / 255. +# D = np.diag(np.random.rand(3)) +# U = orth(np.random.rand(3, 3)) +# conv = np.dot(np.dot(np.transpose(U), D), U) +# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) +# img = np.clip(img, 0.0, 1.0) +# return img + + +def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): + noise_level = random.randint(noise_level1, noise_level2) + rnum = np.random.rand() + if rnum > 0.6: # add color Gaussian noise + img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype( + np.float32 + ) + elif rnum < 0.4: # add grayscale Gaussian noise + img = img + np.random.normal( + 0, noise_level / 255.0, (*img.shape[:2], 1) + ).astype(np.float32) + else: # add noise + L = noise_level2 / 255.0 + D = np.diag(np.random.rand(3)) + U = orth(np.random.rand(3, 3)) + conv = np.dot(np.dot(np.transpose(U), D), U) + img = img + np.random.multivariate_normal( + [0, 0, 0], np.abs(L**2 * conv), img.shape[:2] + ).astype(np.float32) + img = np.clip(img, 0.0, 1.0) + return img + + +def add_speckle_noise(img, noise_level1=2, noise_level2=25): + noise_level = random.randint(noise_level1, noise_level2) + img = np.clip(img, 0.0, 1.0) + rnum = random.random() + if rnum > 0.6: + img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype( + np.float32 + ) + elif rnum < 0.4: + img += img * np.random.normal( + 0, noise_level / 255.0, (*img.shape[:2], 1) + ).astype(np.float32) + else: + L = noise_level2 / 255.0 + D = np.diag(np.random.rand(3)) + U = orth(np.random.rand(3, 3)) + conv = np.dot(np.dot(np.transpose(U), D), U) + img += img * np.random.multivariate_normal( + [0, 0, 0], np.abs(L**2 * conv), img.shape[:2] + ).astype(np.float32) + img = np.clip(img, 0.0, 1.0) + return img + + +def add_Poisson_noise(img): + img = np.clip((img * 255.0).round(), 0, 255) / 255.0 + vals = 10 ** (2 * random.random() + 2.0) # [2, 4] + if random.random() < 0.5: + img = np.random.poisson(img * vals).astype(np.float32) / vals + else: + img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) + img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.0 + noise_gray = ( + np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray + ) + img += noise_gray[:, :, np.newaxis] + img = np.clip(img, 0.0, 1.0) + return img + + +def add_JPEG_noise(img): + quality_factor = random.randint(30, 95) + img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) + result, encimg = cv2.imencode( + ".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor] + ) + img = cv2.imdecode(encimg, 1) + img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) + return img + + +def random_crop(lq, hq, sf=4, lq_patchsize=64): + h, w = lq.shape[:2] + rnd_h = random.randint(0, h - lq_patchsize) + rnd_w = random.randint(0, w - lq_patchsize) + lq = lq[rnd_h : rnd_h + lq_patchsize, rnd_w : rnd_w + lq_patchsize, :] + + rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) + hq = hq[ + rnd_h_H : rnd_h_H + lq_patchsize * sf, rnd_w_H : rnd_w_H + lq_patchsize * sf, : + ] + return lq, hq + + +def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): + """ + This is the degradation model of BSRGAN from the paper + "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" + ---------- + img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) + sf: scale factor + isp_model: camera ISP model + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 + sf_ori = sf + + h1, w1 = img.shape[:2] + img = img.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop + h, w = img.shape[:2] + + if h < lq_patchsize * sf or w < lq_patchsize * sf: + raise ValueError(f"img size ({h1}X{w1}) is too small!") + + hq = img.copy() + + if sf == 4 and random.random() < scale2_prob: # downsample1 + if np.random.rand() < 0.5: + img = cv2.resize( + img, + (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), + interpolation=random.choice([1, 2, 3]), + ) + else: + img = util.imresize_np(img, 1 / 2, True) + img = np.clip(img, 0.0, 1.0) + sf = 2 + + shuffle_order = random.sample(range(7), 7) + idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) + if idx1 > idx2: # keep downsample3 last + shuffle_order[idx1], shuffle_order[idx2] = ( + shuffle_order[idx2], + shuffle_order[idx1], + ) + + for i in shuffle_order: + if i == 0: + img = add_blur(img, sf=sf) + + elif i == 1: + img = add_blur(img, sf=sf) + + elif i == 2: + a, b = img.shape[1], img.shape[0] + # downsample2 + if random.random() < 0.75: + sf1 = random.uniform(1, 2 * sf) + img = cv2.resize( + img, + (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), + interpolation=random.choice([1, 2, 3]), + ) + else: + k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf)) + k_shifted = shift_pixel(k, sf) + k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel + img = ndimage.filters.convolve( + img, np.expand_dims(k_shifted, axis=2), mode="mirror" + ) + img = img[0::sf, 0::sf, ...] # nearest downsampling + img = np.clip(img, 0.0, 1.0) + + elif i == 3: + # downsample3 + img = cv2.resize( + img, + (int(1 / sf * a), int(1 / sf * b)), + interpolation=random.choice([1, 2, 3]), + ) + img = np.clip(img, 0.0, 1.0) + + elif i == 4: + # add Gaussian noise + img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) + + elif i == 5: + # add JPEG noise + if random.random() < jpeg_prob: + img = add_JPEG_noise(img) + + elif i == 6: + # add processed camera sensor noise + if random.random() < isp_prob and isp_model is not None: + with torch.no_grad(): + img, hq = isp_model.forward(img.copy(), hq) + + # add final JPEG compression noise + img = add_JPEG_noise(img) + + # random crop + img, hq = random_crop(img, hq, sf_ori, lq_patchsize) + + return img, hq + + +# todo no isp_model? +def degradation_bsrgan_variant(image, sf=4, isp_model=None): + """ + This is the degradation model of BSRGAN from the paper + "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" + ---------- + sf: scale factor + isp_model: camera ISP model + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + image = util.uint2single(image) + isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 + sf_ori = sf + + h1, w1 = image.shape[:2] + image = image.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop + h, w = image.shape[:2] + + hq = image.copy() + + if sf == 4 and random.random() < scale2_prob: # downsample1 + if np.random.rand() < 0.5: + image = cv2.resize( + image, + (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), + interpolation=random.choice([1, 2, 3]), + ) + else: + image = util.imresize_np(image, 1 / 2, True) + image = np.clip(image, 0.0, 1.0) + sf = 2 + + shuffle_order = random.sample(range(7), 7) + idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) + if idx1 > idx2: # keep downsample3 last + shuffle_order[idx1], shuffle_order[idx2] = ( + shuffle_order[idx2], + shuffle_order[idx1], + ) + + for i in shuffle_order: + if i == 0: + image = add_blur(image, sf=sf) + + elif i == 1: + image = add_blur(image, sf=sf) + + elif i == 2: + a, b = image.shape[1], image.shape[0] + # downsample2 + if random.random() < 0.75: + sf1 = random.uniform(1, 2 * sf) + image = cv2.resize( + image, + (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), + interpolation=random.choice([1, 2, 3]), + ) + else: + k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf)) + k_shifted = shift_pixel(k, sf) + k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel + image = ndimage.filters.convolve( + image, np.expand_dims(k_shifted, axis=2), mode="mirror" + ) + image = image[0::sf, 0::sf, ...] # nearest downsampling + image = np.clip(image, 0.0, 1.0) + + elif i == 3: + # downsample3 + image = cv2.resize( + image, + (int(1 / sf * a), int(1 / sf * b)), + interpolation=random.choice([1, 2, 3]), + ) + image = np.clip(image, 0.0, 1.0) + + elif i == 4: + # add Gaussian noise + image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25) + + elif i == 5: + # add JPEG noise + if random.random() < jpeg_prob: + image = add_JPEG_noise(image) + + # elif i == 6: + # # add processed camera sensor noise + # if random.random() < isp_prob and isp_model is not None: + # with torch.no_grad(): + # img, hq = isp_model.forward(img.copy(), hq) + + # add final JPEG compression noise + image = add_JPEG_noise(image) + image = util.single2uint(image) + example = {"image": image} + return example + + +# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc... +def degradation_bsrgan_plus( + img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None +): + """ + This is an extended degradation model by combining + the degradation models of BSRGAN and Real-ESRGAN + ---------- + img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) + sf: scale factor + use_shuffle: the degradation shuffle + use_sharp: sharpening the img + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + + h1, w1 = img.shape[:2] + img = img.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop + h, w = img.shape[:2] + + if h < lq_patchsize * sf or w < lq_patchsize * sf: + raise ValueError(f"img size ({h1}X{w1}) is too small!") + + if use_sharp: + img = add_sharpening(img) + hq = img.copy() + + if random.random() < shuffle_prob: + shuffle_order = random.sample(range(13), 13) + else: + shuffle_order = list(range(13)) + # local shuffle for noise, JPEG is always the last one + shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6))) + shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13))) + + poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1 + + for i in shuffle_order: + if i == 0: + img = add_blur(img, sf=sf) + elif i == 1: + img = add_resize(img, sf=sf) + elif i == 2: + img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) + elif i == 3: + if random.random() < poisson_prob: + img = add_Poisson_noise(img) + elif i == 4: + if random.random() < speckle_prob: + img = add_speckle_noise(img) + elif i == 5: + if random.random() < isp_prob and isp_model is not None: + with torch.no_grad(): + img, hq = isp_model.forward(img.copy(), hq) + elif i == 6: + img = add_JPEG_noise(img) + elif i == 7: + img = add_blur(img, sf=sf) + elif i == 8: + img = add_resize(img, sf=sf) + elif i == 9: + img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) + elif i == 10: + if random.random() < poisson_prob: + img = add_Poisson_noise(img) + elif i == 11: + if random.random() < speckle_prob: + img = add_speckle_noise(img) + elif i == 12: + if random.random() < isp_prob and isp_model is not None: + with torch.no_grad(): + img, hq = isp_model.forward(img.copy(), hq) + else: + print("check the shuffle!") + + # resize to desired size + img = cv2.resize( + img, + (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])), + interpolation=random.choice([1, 2, 3]), + ) + + # add final JPEG compression noise + img = add_JPEG_noise(img) + + # random crop + img, hq = random_crop(img, hq, sf, lq_patchsize) + + return img, hq + + +if __name__ == "__main__": + print("hey") + img = util.imread_uint("utils/test.png", 3) + print(img) + img = util.uint2single(img) + print(img) + img = img[:448, :448] + h = img.shape[0] // 4 + print("resizing to", h) + sf = 4 + deg_fn = partial(degradation_bsrgan_variant, sf=sf) + for i in range(20): + print(i) + img_lq = deg_fn(img) + print(img_lq) + img_lq_bicubic = albumentations.SmallestMaxSize( + max_size=h, interpolation=cv2.INTER_CUBIC + )(image=img)["image"] + print(img_lq.shape) + print("bicubic", img_lq_bicubic.shape) + print(img_hq.shape) + lq_nearest = cv2.resize( + util.single2uint(img_lq), + (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), + interpolation=0, + ) + lq_bicubic_nearest = cv2.resize( + util.single2uint(img_lq_bicubic), + (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), + interpolation=0, + ) + img_concat = np.concatenate( + [lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1 + ) + util.imsave(img_concat, str(i) + ".png") diff --git a/extern/ldm_zero123/modules/image_degradation/bsrgan_light.py b/extern/ldm_zero123/modules/image_degradation/bsrgan_light.py new file mode 100755 index 0000000..84318a7 --- /dev/null +++ b/extern/ldm_zero123/modules/image_degradation/bsrgan_light.py @@ -0,0 +1,720 @@ +# -*- coding: utf-8 -*- +import random +from functools import partial + +import albumentations +import cv2 +import numpy as np +import scipy +import scipy.stats as ss +import torch +from scipy import ndimage +from scipy.interpolate import interp2d +from scipy.linalg import orth + +import extern.ldm_zero123.modules.image_degradation.utils_image as util + +""" +# -------------------------------------------- +# Super-Resolution +# -------------------------------------------- +# +# Kai Zhang (cskaizhang@gmail.com) +# https://github.com/cszn +# From 2019/03--2021/08 +# -------------------------------------------- +""" + + +def modcrop_np(img, sf): + """ + Args: + img: numpy image, WxH or WxHxC + sf: scale factor + Return: + cropped image + """ + w, h = img.shape[:2] + im = np.copy(img) + return im[: w - w % sf, : h - h % sf, ...] + + +""" +# -------------------------------------------- +# anisotropic Gaussian kernels +# -------------------------------------------- +""" + + +def analytic_kernel(k): + """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" + k_size = k.shape[0] + # Calculate the big kernels size + big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) + # Loop over the small kernel to fill the big one + for r in range(k_size): + for c in range(k_size): + big_k[2 * r : 2 * r + k_size, 2 * c : 2 * c + k_size] += k[r, c] * k + # Crop the edges of the big kernel to ignore very small values and increase run time of SR + crop = k_size // 2 + cropped_big_k = big_k[crop:-crop, crop:-crop] + # Normalize to 1 + return cropped_big_k / cropped_big_k.sum() + + +def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): + """generate an anisotropic Gaussian kernel + Args: + ksize : e.g., 15, kernel size + theta : [0, pi], rotation angle range + l1 : [0.1,50], scaling of eigenvalues + l2 : [0.1,l1], scaling of eigenvalues + If l1 = l2, will get an isotropic Gaussian kernel. + Returns: + k : kernel + """ + + v = np.dot( + np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), + np.array([1.0, 0.0]), + ) + V = np.array([[v[0], v[1]], [v[1], -v[0]]]) + D = np.array([[l1, 0], [0, l2]]) + Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) + k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) + + return k + + +def gm_blur_kernel(mean, cov, size=15): + center = size / 2.0 + 0.5 + k = np.zeros([size, size]) + for y in range(size): + for x in range(size): + cy = y - center + 1 + cx = x - center + 1 + k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) + + k = k / np.sum(k) + return k + + +def shift_pixel(x, sf, upper_left=True): + """shift pixel for super-resolution with different scale factors + Args: + x: WxHxC or WxH + sf: scale factor + upper_left: shift direction + """ + h, w = x.shape[:2] + shift = (sf - 1) * 0.5 + xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) + if upper_left: + x1 = xv + shift + y1 = yv + shift + else: + x1 = xv - shift + y1 = yv - shift + + x1 = np.clip(x1, 0, w - 1) + y1 = np.clip(y1, 0, h - 1) + + if x.ndim == 2: + x = interp2d(xv, yv, x)(x1, y1) + if x.ndim == 3: + for i in range(x.shape[-1]): + x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) + + return x + + +def blur(x, k): + """ + x: image, NxcxHxW + k: kernel, Nx1xhxw + """ + n, c = x.shape[:2] + p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 + x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode="replicate") + k = k.repeat(1, c, 1, 1) + k = k.view(-1, 1, k.shape[2], k.shape[3]) + x = x.view(1, -1, x.shape[2], x.shape[3]) + x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) + x = x.view(n, c, x.shape[2], x.shape[3]) + + return x + + +def gen_kernel( + k_size=np.array([15, 15]), + scale_factor=np.array([4, 4]), + min_var=0.6, + max_var=10.0, + noise_level=0, +): + """ " + # modified version of https://github.com/assafshocher/BlindSR_dataset_generator + # Kai Zhang + # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var + # max_var = 2.5 * sf + """ + # Set random eigen-vals (lambdas) and angle (theta) for COV matrix + lambda_1 = min_var + np.random.rand() * (max_var - min_var) + lambda_2 = min_var + np.random.rand() * (max_var - min_var) + theta = np.random.rand() * np.pi # random theta + noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 + + # Set COV matrix using Lambdas and Theta + LAMBDA = np.diag([lambda_1, lambda_2]) + Q = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) + SIGMA = Q @ LAMBDA @ Q.T + INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] + + # Set expectation position (shifting kernel for aligned image) + MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) + MU = MU[None, None, :, None] + + # Create meshgrid for Gaussian + [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) + Z = np.stack([X, Y], 2)[:, :, :, None] + + # Calcualte Gaussian for every pixel of the kernel + ZZ = Z - MU + ZZ_t = ZZ.transpose(0, 1, 3, 2) + raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) + + # shift the kernel so it will be centered + # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) + + # Normalize the kernel and return + # kernel = raw_kernel_centered / np.sum(raw_kernel_centered) + kernel = raw_kernel / np.sum(raw_kernel) + return kernel + + +def fspecial_gaussian(hsize, sigma): + hsize = [hsize, hsize] + siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] + std = sigma + [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) + arg = -(x * x + y * y) / (2 * std * std) + h = np.exp(arg) + h[h < scipy.finfo(float).eps * h.max()] = 0 + sumh = h.sum() + if sumh != 0: + h = h / sumh + return h + + +def fspecial_laplacian(alpha): + alpha = max([0, min([alpha, 1])]) + h1 = alpha / (alpha + 1) + h2 = (1 - alpha) / (alpha + 1) + h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] + h = np.array(h) + return h + + +def fspecial(filter_type, *args, **kwargs): + """ + python code from: + https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py + """ + if filter_type == "gaussian": + return fspecial_gaussian(*args, **kwargs) + if filter_type == "laplacian": + return fspecial_laplacian(*args, **kwargs) + + +""" +# -------------------------------------------- +# degradation models +# -------------------------------------------- +""" + + +def bicubic_degradation(x, sf=3): + """ + Args: + x: HxWxC image, [0, 1] + sf: down-scale factor + Return: + bicubicly downsampled LR image + """ + x = util.imresize_np(x, scale=1 / sf) + return x + + +def srmd_degradation(x, k, sf=3): + """blur + bicubic downsampling + Args: + x: HxWxC image, [0, 1] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + Reference: + @inproceedings{zhang2018learning, + title={Learning a single convolutional super-resolution network for multiple degradations}, + author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + pages={3262--3271}, + year={2018} + } + """ + x = ndimage.convolve( + x, np.expand_dims(k, axis=2), mode="wrap" + ) # 'nearest' | 'mirror' + x = bicubic_degradation(x, sf=sf) + return x + + +def dpsr_degradation(x, k, sf=3): + """bicubic downsampling + blur + Args: + x: HxWxC image, [0, 1] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + Reference: + @inproceedings{zhang2019deep, + title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, + author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + pages={1671--1681}, + year={2019} + } + """ + x = bicubic_degradation(x, sf=sf) + x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode="wrap") + return x + + +def classical_degradation(x, k, sf=3): + """blur + downsampling + Args: + x: HxWxC image, [0, 1]/[0, 255] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + """ + x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode="wrap") + # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) + st = 0 + return x[st::sf, st::sf, ...] + + +def add_sharpening(img, weight=0.5, radius=50, threshold=10): + """USM sharpening. borrowed from real-ESRGAN + Input image: I; Blurry image: B. + 1. K = I + weight * (I - B) + 2. Mask = 1 if abs(I - B) > threshold, else: 0 + 3. Blur mask: + 4. Out = Mask * K + (1 - Mask) * I + Args: + img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. + weight (float): Sharp weight. Default: 1. + radius (float): Kernel size of Gaussian blur. Default: 50. + threshold (int): + """ + if radius % 2 == 0: + radius += 1 + blur = cv2.GaussianBlur(img, (radius, radius), 0) + residual = img - blur + mask = np.abs(residual) * 255 > threshold + mask = mask.astype("float32") + soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) + + K = img + weight * residual + K = np.clip(K, 0, 1) + return soft_mask * K + (1 - soft_mask) * img + + +def add_blur(img, sf=4): + wd2 = 4.0 + sf + wd = 2.0 + 0.2 * sf + + wd2 = wd2 / 4 + wd = wd / 4 + + if random.random() < 0.5: + l1 = wd2 * random.random() + l2 = wd2 * random.random() + k = anisotropic_Gaussian( + ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2 + ) + else: + k = fspecial("gaussian", random.randint(2, 4) + 3, wd * random.random()) + img = ndimage.convolve(img, np.expand_dims(k, axis=2), mode="mirror") + + return img + + +def add_resize(img, sf=4): + rnum = np.random.rand() + if rnum > 0.8: # up + sf1 = random.uniform(1, 2) + elif rnum < 0.7: # down + sf1 = random.uniform(0.5 / sf, 1) + else: + sf1 = 1.0 + img = cv2.resize( + img, + (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), + interpolation=random.choice([1, 2, 3]), + ) + img = np.clip(img, 0.0, 1.0) + + return img + + +# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): +# noise_level = random.randint(noise_level1, noise_level2) +# rnum = np.random.rand() +# if rnum > 0.6: # add color Gaussian noise +# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) +# elif rnum < 0.4: # add grayscale Gaussian noise +# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) +# else: # add noise +# L = noise_level2 / 255. +# D = np.diag(np.random.rand(3)) +# U = orth(np.random.rand(3, 3)) +# conv = np.dot(np.dot(np.transpose(U), D), U) +# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) +# img = np.clip(img, 0.0, 1.0) +# return img + + +def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): + noise_level = random.randint(noise_level1, noise_level2) + rnum = np.random.rand() + if rnum > 0.6: # add color Gaussian noise + img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype( + np.float32 + ) + elif rnum < 0.4: # add grayscale Gaussian noise + img = img + np.random.normal( + 0, noise_level / 255.0, (*img.shape[:2], 1) + ).astype(np.float32) + else: # add noise + L = noise_level2 / 255.0 + D = np.diag(np.random.rand(3)) + U = orth(np.random.rand(3, 3)) + conv = np.dot(np.dot(np.transpose(U), D), U) + img = img + np.random.multivariate_normal( + [0, 0, 0], np.abs(L**2 * conv), img.shape[:2] + ).astype(np.float32) + img = np.clip(img, 0.0, 1.0) + return img + + +def add_speckle_noise(img, noise_level1=2, noise_level2=25): + noise_level = random.randint(noise_level1, noise_level2) + img = np.clip(img, 0.0, 1.0) + rnum = random.random() + if rnum > 0.6: + img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype( + np.float32 + ) + elif rnum < 0.4: + img += img * np.random.normal( + 0, noise_level / 255.0, (*img.shape[:2], 1) + ).astype(np.float32) + else: + L = noise_level2 / 255.0 + D = np.diag(np.random.rand(3)) + U = orth(np.random.rand(3, 3)) + conv = np.dot(np.dot(np.transpose(U), D), U) + img += img * np.random.multivariate_normal( + [0, 0, 0], np.abs(L**2 * conv), img.shape[:2] + ).astype(np.float32) + img = np.clip(img, 0.0, 1.0) + return img + + +def add_Poisson_noise(img): + img = np.clip((img * 255.0).round(), 0, 255) / 255.0 + vals = 10 ** (2 * random.random() + 2.0) # [2, 4] + if random.random() < 0.5: + img = np.random.poisson(img * vals).astype(np.float32) / vals + else: + img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) + img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.0 + noise_gray = ( + np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray + ) + img += noise_gray[:, :, np.newaxis] + img = np.clip(img, 0.0, 1.0) + return img + + +def add_JPEG_noise(img): + quality_factor = random.randint(80, 95) + img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) + result, encimg = cv2.imencode( + ".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor] + ) + img = cv2.imdecode(encimg, 1) + img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) + return img + + +def random_crop(lq, hq, sf=4, lq_patchsize=64): + h, w = lq.shape[:2] + rnd_h = random.randint(0, h - lq_patchsize) + rnd_w = random.randint(0, w - lq_patchsize) + lq = lq[rnd_h : rnd_h + lq_patchsize, rnd_w : rnd_w + lq_patchsize, :] + + rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) + hq = hq[ + rnd_h_H : rnd_h_H + lq_patchsize * sf, rnd_w_H : rnd_w_H + lq_patchsize * sf, : + ] + return lq, hq + + +def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): + """ + This is the degradation model of BSRGAN from the paper + "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" + ---------- + img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) + sf: scale factor + isp_model: camera ISP model + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 + sf_ori = sf + + h1, w1 = img.shape[:2] + img = img.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop + h, w = img.shape[:2] + + if h < lq_patchsize * sf or w < lq_patchsize * sf: + raise ValueError(f"img size ({h1}X{w1}) is too small!") + + hq = img.copy() + + if sf == 4 and random.random() < scale2_prob: # downsample1 + if np.random.rand() < 0.5: + img = cv2.resize( + img, + (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), + interpolation=random.choice([1, 2, 3]), + ) + else: + img = util.imresize_np(img, 1 / 2, True) + img = np.clip(img, 0.0, 1.0) + sf = 2 + + shuffle_order = random.sample(range(7), 7) + idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) + if idx1 > idx2: # keep downsample3 last + shuffle_order[idx1], shuffle_order[idx2] = ( + shuffle_order[idx2], + shuffle_order[idx1], + ) + + for i in shuffle_order: + if i == 0: + img = add_blur(img, sf=sf) + + elif i == 1: + img = add_blur(img, sf=sf) + + elif i == 2: + a, b = img.shape[1], img.shape[0] + # downsample2 + if random.random() < 0.75: + sf1 = random.uniform(1, 2 * sf) + img = cv2.resize( + img, + (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), + interpolation=random.choice([1, 2, 3]), + ) + else: + k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf)) + k_shifted = shift_pixel(k, sf) + k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel + img = ndimage.convolve( + img, np.expand_dims(k_shifted, axis=2), mode="mirror" + ) + img = img[0::sf, 0::sf, ...] # nearest downsampling + img = np.clip(img, 0.0, 1.0) + + elif i == 3: + # downsample3 + img = cv2.resize( + img, + (int(1 / sf * a), int(1 / sf * b)), + interpolation=random.choice([1, 2, 3]), + ) + img = np.clip(img, 0.0, 1.0) + + elif i == 4: + # add Gaussian noise + img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8) + + elif i == 5: + # add JPEG noise + if random.random() < jpeg_prob: + img = add_JPEG_noise(img) + + elif i == 6: + # add processed camera sensor noise + if random.random() < isp_prob and isp_model is not None: + with torch.no_grad(): + img, hq = isp_model.forward(img.copy(), hq) + + # add final JPEG compression noise + img = add_JPEG_noise(img) + + # random crop + img, hq = random_crop(img, hq, sf_ori, lq_patchsize) + + return img, hq + + +# todo no isp_model? +def degradation_bsrgan_variant(image, sf=4, isp_model=None): + """ + This is the degradation model of BSRGAN from the paper + "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" + ---------- + sf: scale factor + isp_model: camera ISP model + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + image = util.uint2single(image) + isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 + sf_ori = sf + + h1, w1 = image.shape[:2] + image = image.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop + h, w = image.shape[:2] + + hq = image.copy() + + if sf == 4 and random.random() < scale2_prob: # downsample1 + if np.random.rand() < 0.5: + image = cv2.resize( + image, + (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), + interpolation=random.choice([1, 2, 3]), + ) + else: + image = util.imresize_np(image, 1 / 2, True) + image = np.clip(image, 0.0, 1.0) + sf = 2 + + shuffle_order = random.sample(range(7), 7) + idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) + if idx1 > idx2: # keep downsample3 last + shuffle_order[idx1], shuffle_order[idx2] = ( + shuffle_order[idx2], + shuffle_order[idx1], + ) + + for i in shuffle_order: + if i == 0: + image = add_blur(image, sf=sf) + + # elif i == 1: + # image = add_blur(image, sf=sf) + + if i == 0: + pass + + elif i == 2: + a, b = image.shape[1], image.shape[0] + # downsample2 + if random.random() < 0.8: + sf1 = random.uniform(1, 2 * sf) + image = cv2.resize( + image, + (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), + interpolation=random.choice([1, 2, 3]), + ) + else: + k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf)) + k_shifted = shift_pixel(k, sf) + k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel + image = ndimage.convolve( + image, np.expand_dims(k_shifted, axis=2), mode="mirror" + ) + image = image[0::sf, 0::sf, ...] # nearest downsampling + + image = np.clip(image, 0.0, 1.0) + + elif i == 3: + # downsample3 + image = cv2.resize( + image, + (int(1 / sf * a), int(1 / sf * b)), + interpolation=random.choice([1, 2, 3]), + ) + image = np.clip(image, 0.0, 1.0) + + elif i == 4: + # add Gaussian noise + image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2) + + elif i == 5: + # add JPEG noise + if random.random() < jpeg_prob: + image = add_JPEG_noise(image) + # + # elif i == 6: + # # add processed camera sensor noise + # if random.random() < isp_prob and isp_model is not None: + # with torch.no_grad(): + # img, hq = isp_model.forward(img.copy(), hq) + + # add final JPEG compression noise + image = add_JPEG_noise(image) + image = util.single2uint(image) + example = {"image": image} + return example + + +if __name__ == "__main__": + print("hey") + img = util.imread_uint("utils/test.png", 3) + img = img[:448, :448] + h = img.shape[0] // 4 + print("resizing to", h) + sf = 4 + deg_fn = partial(degradation_bsrgan_variant, sf=sf) + for i in range(20): + print(i) + img_hq = img + img_lq = deg_fn(img)["image"] + img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq) + print(img_lq) + img_lq_bicubic = albumentations.SmallestMaxSize( + max_size=h, interpolation=cv2.INTER_CUBIC + )(image=img_hq)["image"] + print(img_lq.shape) + print("bicubic", img_lq_bicubic.shape) + print(img_hq.shape) + lq_nearest = cv2.resize( + util.single2uint(img_lq), + (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), + interpolation=0, + ) + lq_bicubic_nearest = cv2.resize( + util.single2uint(img_lq_bicubic), + (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), + interpolation=0, + ) + img_concat = np.concatenate( + [lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1 + ) + util.imsave(img_concat, str(i) + ".png") diff --git a/extern/ldm_zero123/modules/image_degradation/utils/test.png b/extern/ldm_zero123/modules/image_degradation/utils/test.png new file mode 100755 index 0000000..4249b43 Binary files /dev/null and b/extern/ldm_zero123/modules/image_degradation/utils/test.png differ diff --git a/extern/ldm_zero123/modules/image_degradation/utils_image.py b/extern/ldm_zero123/modules/image_degradation/utils_image.py new file mode 100755 index 0000000..c933af5 --- /dev/null +++ b/extern/ldm_zero123/modules/image_degradation/utils_image.py @@ -0,0 +1,988 @@ +import math +import os +import random +from datetime import datetime + +import cv2 +import numpy as np +import torch +from torchvision.utils import make_grid + +# import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py + + +os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" + + +""" +# -------------------------------------------- +# Kai Zhang (github: https://github.com/cszn) +# 03/Mar/2019 +# -------------------------------------------- +# https://github.com/twhui/SRGAN-pyTorch +# https://github.com/xinntao/BasicSR +# -------------------------------------------- +""" + + +IMG_EXTENSIONS = [ + ".jpg", + ".JPG", + ".jpeg", + ".JPEG", + ".png", + ".PNG", + ".ppm", + ".PPM", + ".bmp", + ".BMP", + ".tif", +] + + +def is_image_file(filename): + return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) + + +def get_timestamp(): + return datetime.now().strftime("%y%m%d-%H%M%S") + + +def imshow(x, title=None, cbar=False, figsize=None): + plt.figure(figsize=figsize) + plt.imshow(np.squeeze(x), interpolation="nearest", cmap="gray") + if title: + plt.title(title) + if cbar: + plt.colorbar() + plt.show() + + +def surf(Z, cmap="rainbow", figsize=None): + plt.figure(figsize=figsize) + ax3 = plt.axes(projection="3d") + + w, h = Z.shape[:2] + xx = np.arange(0, w, 1) + yy = np.arange(0, h, 1) + X, Y = np.meshgrid(xx, yy) + ax3.plot_surface(X, Y, Z, cmap=cmap) + # ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap) + plt.show() + + +""" +# -------------------------------------------- +# get image pathes +# -------------------------------------------- +""" + + +def get_image_paths(dataroot): + paths = None # return None if dataroot is None + if dataroot is not None: + paths = sorted(_get_paths_from_images(dataroot)) + return paths + + +def _get_paths_from_images(path): + assert os.path.isdir(path), "{:s} is not a valid directory".format(path) + images = [] + for dirpath, _, fnames in sorted(os.walk(path)): + for fname in sorted(fnames): + if is_image_file(fname): + img_path = os.path.join(dirpath, fname) + images.append(img_path) + assert images, "{:s} has no valid image file".format(path) + return images + + +""" +# -------------------------------------------- +# split large images into small images +# -------------------------------------------- +""" + + +def patches_from_image(img, p_size=512, p_overlap=64, p_max=800): + w, h = img.shape[:2] + patches = [] + if w > p_max and h > p_max: + w1 = list(np.arange(0, w - p_size, p_size - p_overlap, dtype=np.int)) + h1 = list(np.arange(0, h - p_size, p_size - p_overlap, dtype=np.int)) + w1.append(w - p_size) + h1.append(h - p_size) + # print(w1) + # print(h1) + for i in w1: + for j in h1: + patches.append(img[i : i + p_size, j : j + p_size, :]) + else: + patches.append(img) + + return patches + + +def imssave(imgs, img_path): + """ + imgs: list, N images of size WxHxC + """ + img_name, ext = os.path.splitext(os.path.basename(img_path)) + + for i, img in enumerate(imgs): + if img.ndim == 3: + img = img[:, :, [2, 1, 0]] + new_path = os.path.join( + os.path.dirname(img_path), img_name + str("_s{:04d}".format(i)) + ".png" + ) + cv2.imwrite(new_path, img) + + +def split_imageset( + original_dataroot, + taget_dataroot, + n_channels=3, + p_size=800, + p_overlap=96, + p_max=1000, +): + """ + split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size), + and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max) + will be splitted. + Args: + original_dataroot: + taget_dataroot: + p_size: size of small images + p_overlap: patch size in training is a good choice + p_max: images with smaller size than (p_max)x(p_max) keep unchanged. + """ + paths = get_image_paths(original_dataroot) + for img_path in paths: + # img_name, ext = os.path.splitext(os.path.basename(img_path)) + img = imread_uint(img_path, n_channels=n_channels) + patches = patches_from_image(img, p_size, p_overlap, p_max) + imssave(patches, os.path.join(taget_dataroot, os.path.basename(img_path))) + # if original_dataroot == taget_dataroot: + # del img_path + + +""" +# -------------------------------------------- +# makedir +# -------------------------------------------- +""" + + +def mkdir(path): + if not os.path.exists(path): + os.makedirs(path) + + +def mkdirs(paths): + if isinstance(paths, str): + mkdir(paths) + else: + for path in paths: + mkdir(path) + + +def mkdir_and_rename(path): + if os.path.exists(path): + new_name = path + "_archived_" + get_timestamp() + print("Path already exists. Rename it to [{:s}]".format(new_name)) + os.rename(path, new_name) + os.makedirs(path) + + +""" +# -------------------------------------------- +# read image from path +# opencv is fast, but read BGR numpy image +# -------------------------------------------- +""" + + +# -------------------------------------------- +# get uint8 image of size HxWxn_channles (RGB) +# -------------------------------------------- +def imread_uint(path, n_channels=3): + # input: path + # output: HxWx3(RGB or GGG), or HxWx1 (G) + if n_channels == 1: + img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE + img = np.expand_dims(img, axis=2) # HxWx1 + elif n_channels == 3: + img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G + if img.ndim == 2: + img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG + else: + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB + return img + + +# -------------------------------------------- +# matlab's imwrite +# -------------------------------------------- +def imsave(img, img_path): + img = np.squeeze(img) + if img.ndim == 3: + img = img[:, :, [2, 1, 0]] + cv2.imwrite(img_path, img) + + +def imwrite(img, img_path): + img = np.squeeze(img) + if img.ndim == 3: + img = img[:, :, [2, 1, 0]] + cv2.imwrite(img_path, img) + + +# -------------------------------------------- +# get single image of size HxWxn_channles (BGR) +# -------------------------------------------- +def read_img(path): + # read image by cv2 + # return: Numpy float32, HWC, BGR, [0,1] + img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE + img = img.astype(np.float32) / 255.0 + if img.ndim == 2: + img = np.expand_dims(img, axis=2) + # some images have 4 channels + if img.shape[2] > 3: + img = img[:, :, :3] + return img + + +""" +# -------------------------------------------- +# image format conversion +# -------------------------------------------- +# numpy(single) <---> numpy(unit) +# numpy(single) <---> tensor +# numpy(unit) <---> tensor +# -------------------------------------------- +""" + + +# -------------------------------------------- +# numpy(single) [0, 1] <---> numpy(unit) +# -------------------------------------------- + + +def uint2single(img): + return np.float32(img / 255.0) + + +def single2uint(img): + return np.uint8((img.clip(0, 1) * 255.0).round()) + + +def uint162single(img): + return np.float32(img / 65535.0) + + +def single2uint16(img): + return np.uint16((img.clip(0, 1) * 65535.0).round()) + + +# -------------------------------------------- +# numpy(unit) (HxWxC or HxW) <---> tensor +# -------------------------------------------- + + +# convert uint to 4-dimensional torch tensor +def uint2tensor4(img): + if img.ndim == 2: + img = np.expand_dims(img, axis=2) + return ( + torch.from_numpy(np.ascontiguousarray(img)) + .permute(2, 0, 1) + .float() + .div(255.0) + .unsqueeze(0) + ) + + +# convert uint to 3-dimensional torch tensor +def uint2tensor3(img): + if img.ndim == 2: + img = np.expand_dims(img, axis=2) + return ( + torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.0) + ) + + +# convert 2/3/4-dimensional torch tensor to uint +def tensor2uint(img): + img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy() + if img.ndim == 3: + img = np.transpose(img, (1, 2, 0)) + return np.uint8((img * 255.0).round()) + + +# -------------------------------------------- +# numpy(single) (HxWxC) <---> tensor +# -------------------------------------------- + + +# convert single (HxWxC) to 3-dimensional torch tensor +def single2tensor3(img): + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float() + + +# convert single (HxWxC) to 4-dimensional torch tensor +def single2tensor4(img): + return ( + torch.from_numpy(np.ascontiguousarray(img)) + .permute(2, 0, 1) + .float() + .unsqueeze(0) + ) + + +# convert torch tensor to single +def tensor2single(img): + img = img.data.squeeze().float().cpu().numpy() + if img.ndim == 3: + img = np.transpose(img, (1, 2, 0)) + + return img + + +# convert torch tensor to single +def tensor2single3(img): + img = img.data.squeeze().float().cpu().numpy() + if img.ndim == 3: + img = np.transpose(img, (1, 2, 0)) + elif img.ndim == 2: + img = np.expand_dims(img, axis=2) + return img + + +def single2tensor5(img): + return ( + torch.from_numpy(np.ascontiguousarray(img)) + .permute(2, 0, 1, 3) + .float() + .unsqueeze(0) + ) + + +def single32tensor5(img): + return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0) + + +def single42tensor4(img): + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float() + + +# from skimage.io import imread, imsave +def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)): + """ + Converts a torch Tensor into an image Numpy array of BGR channel order + Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order + Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default) + """ + tensor = ( + tensor.squeeze().float().cpu().clamp_(*min_max) + ) # squeeze first, then clamp + tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1] + n_dim = tensor.dim() + if n_dim == 4: + n_img = len(tensor) + img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy() + img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR + elif n_dim == 3: + img_np = tensor.numpy() + img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR + elif n_dim == 2: + img_np = tensor.numpy() + else: + raise TypeError( + "Only support 4D, 3D and 2D tensor. But received with dimension: {:d}".format( + n_dim + ) + ) + if out_type == np.uint8: + img_np = (img_np * 255.0).round() + # Important. Unlike matlab, numpy.unit8() WILL NOT round by default. + return img_np.astype(out_type) + + +""" +# -------------------------------------------- +# Augmentation, flipe and/or rotate +# -------------------------------------------- +# The following two are enough. +# (1) augmet_img: numpy image of WxHxC or WxH +# (2) augment_img_tensor4: tensor image 1xCxWxH +# -------------------------------------------- +""" + + +def augment_img(img, mode=0): + """Kai Zhang (github: https://github.com/cszn)""" + if mode == 0: + return img + elif mode == 1: + return np.flipud(np.rot90(img)) + elif mode == 2: + return np.flipud(img) + elif mode == 3: + return np.rot90(img, k=3) + elif mode == 4: + return np.flipud(np.rot90(img, k=2)) + elif mode == 5: + return np.rot90(img) + elif mode == 6: + return np.rot90(img, k=2) + elif mode == 7: + return np.flipud(np.rot90(img, k=3)) + + +def augment_img_tensor4(img, mode=0): + """Kai Zhang (github: https://github.com/cszn)""" + if mode == 0: + return img + elif mode == 1: + return img.rot90(1, [2, 3]).flip([2]) + elif mode == 2: + return img.flip([2]) + elif mode == 3: + return img.rot90(3, [2, 3]) + elif mode == 4: + return img.rot90(2, [2, 3]).flip([2]) + elif mode == 5: + return img.rot90(1, [2, 3]) + elif mode == 6: + return img.rot90(2, [2, 3]) + elif mode == 7: + return img.rot90(3, [2, 3]).flip([2]) + + +def augment_img_tensor(img, mode=0): + """Kai Zhang (github: https://github.com/cszn)""" + img_size = img.size() + img_np = img.data.cpu().numpy() + if len(img_size) == 3: + img_np = np.transpose(img_np, (1, 2, 0)) + elif len(img_size) == 4: + img_np = np.transpose(img_np, (2, 3, 1, 0)) + img_np = augment_img(img_np, mode=mode) + img_tensor = torch.from_numpy(np.ascontiguousarray(img_np)) + if len(img_size) == 3: + img_tensor = img_tensor.permute(2, 0, 1) + elif len(img_size) == 4: + img_tensor = img_tensor.permute(3, 2, 0, 1) + + return img_tensor.type_as(img) + + +def augment_img_np3(img, mode=0): + if mode == 0: + return img + elif mode == 1: + return img.transpose(1, 0, 2) + elif mode == 2: + return img[::-1, :, :] + elif mode == 3: + img = img[::-1, :, :] + img = img.transpose(1, 0, 2) + return img + elif mode == 4: + return img[:, ::-1, :] + elif mode == 5: + img = img[:, ::-1, :] + img = img.transpose(1, 0, 2) + return img + elif mode == 6: + img = img[:, ::-1, :] + img = img[::-1, :, :] + return img + elif mode == 7: + img = img[:, ::-1, :] + img = img[::-1, :, :] + img = img.transpose(1, 0, 2) + return img + + +def augment_imgs(img_list, hflip=True, rot=True): + # horizontal flip OR rotate + hflip = hflip and random.random() < 0.5 + vflip = rot and random.random() < 0.5 + rot90 = rot and random.random() < 0.5 + + def _augment(img): + if hflip: + img = img[:, ::-1, :] + if vflip: + img = img[::-1, :, :] + if rot90: + img = img.transpose(1, 0, 2) + return img + + return [_augment(img) for img in img_list] + + +""" +# -------------------------------------------- +# modcrop and shave +# -------------------------------------------- +""" + + +def modcrop(img_in, scale): + # img_in: Numpy, HWC or HW + img = np.copy(img_in) + if img.ndim == 2: + H, W = img.shape + H_r, W_r = H % scale, W % scale + img = img[: H - H_r, : W - W_r] + elif img.ndim == 3: + H, W, C = img.shape + H_r, W_r = H % scale, W % scale + img = img[: H - H_r, : W - W_r, :] + else: + raise ValueError("Wrong img ndim: [{:d}].".format(img.ndim)) + return img + + +def shave(img_in, border=0): + # img_in: Numpy, HWC or HW + img = np.copy(img_in) + h, w = img.shape[:2] + img = img[border : h - border, border : w - border] + return img + + +""" +# -------------------------------------------- +# image processing process on numpy image +# channel_convert(in_c, tar_type, img_list): +# rgb2ycbcr(img, only_y=True): +# bgr2ycbcr(img, only_y=True): +# ycbcr2rgb(img): +# -------------------------------------------- +""" + + +def rgb2ycbcr(img, only_y=True): + """same as matlab rgb2ycbcr + only_y: only return Y channel + Input: + uint8, [0, 255] + float, [0, 1] + """ + in_img_type = img.dtype + img.astype(np.float32) + if in_img_type != np.uint8: + img *= 255.0 + # convert + if only_y: + rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0 + else: + rlt = np.matmul( + img, + [ + [65.481, -37.797, 112.0], + [128.553, -74.203, -93.786], + [24.966, 112.0, -18.214], + ], + ) / 255.0 + [16, 128, 128] + if in_img_type == np.uint8: + rlt = rlt.round() + else: + rlt /= 255.0 + return rlt.astype(in_img_type) + + +def ycbcr2rgb(img): + """same as matlab ycbcr2rgb + Input: + uint8, [0, 255] + float, [0, 1] + """ + in_img_type = img.dtype + img.astype(np.float32) + if in_img_type != np.uint8: + img *= 255.0 + # convert + rlt = np.matmul( + img, + [ + [0.00456621, 0.00456621, 0.00456621], + [0, -0.00153632, 0.00791071], + [0.00625893, -0.00318811, 0], + ], + ) * 255.0 + [-222.921, 135.576, -276.836] + if in_img_type == np.uint8: + rlt = rlt.round() + else: + rlt /= 255.0 + return rlt.astype(in_img_type) + + +def bgr2ycbcr(img, only_y=True): + """bgr version of rgb2ycbcr + only_y: only return Y channel + Input: + uint8, [0, 255] + float, [0, 1] + """ + in_img_type = img.dtype + img.astype(np.float32) + if in_img_type != np.uint8: + img *= 255.0 + # convert + if only_y: + rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0 + else: + rlt = np.matmul( + img, + [ + [24.966, 112.0, -18.214], + [128.553, -74.203, -93.786], + [65.481, -37.797, 112.0], + ], + ) / 255.0 + [16, 128, 128] + if in_img_type == np.uint8: + rlt = rlt.round() + else: + rlt /= 255.0 + return rlt.astype(in_img_type) + + +def channel_convert(in_c, tar_type, img_list): + # conversion among BGR, gray and y + if in_c == 3 and tar_type == "gray": # BGR to gray + gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list] + return [np.expand_dims(img, axis=2) for img in gray_list] + elif in_c == 3 and tar_type == "y": # BGR to y + y_list = [bgr2ycbcr(img, only_y=True) for img in img_list] + return [np.expand_dims(img, axis=2) for img in y_list] + elif in_c == 1 and tar_type == "RGB": # gray/y to BGR + return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list] + else: + return img_list + + +""" +# -------------------------------------------- +# metric, PSNR and SSIM +# -------------------------------------------- +""" + + +# -------------------------------------------- +# PSNR +# -------------------------------------------- +def calculate_psnr(img1, img2, border=0): + # img1 and img2 have range [0, 255] + # img1 = img1.squeeze() + # img2 = img2.squeeze() + if not img1.shape == img2.shape: + raise ValueError("Input images must have the same dimensions.") + h, w = img1.shape[:2] + img1 = img1[border : h - border, border : w - border] + img2 = img2[border : h - border, border : w - border] + + img1 = img1.astype(np.float64) + img2 = img2.astype(np.float64) + mse = np.mean((img1 - img2) ** 2) + if mse == 0: + return float("inf") + return 20 * math.log10(255.0 / math.sqrt(mse)) + + +# -------------------------------------------- +# SSIM +# -------------------------------------------- +def calculate_ssim(img1, img2, border=0): + """calculate SSIM + the same outputs as MATLAB's + img1, img2: [0, 255] + """ + # img1 = img1.squeeze() + # img2 = img2.squeeze() + if not img1.shape == img2.shape: + raise ValueError("Input images must have the same dimensions.") + h, w = img1.shape[:2] + img1 = img1[border : h - border, border : w - border] + img2 = img2[border : h - border, border : w - border] + + if img1.ndim == 2: + return ssim(img1, img2) + elif img1.ndim == 3: + if img1.shape[2] == 3: + ssims = [] + for i in range(3): + ssims.append(ssim(img1[:, :, i], img2[:, :, i])) + return np.array(ssims).mean() + elif img1.shape[2] == 1: + return ssim(np.squeeze(img1), np.squeeze(img2)) + else: + raise ValueError("Wrong input image dimensions.") + + +def ssim(img1, img2): + C1 = (0.01 * 255) ** 2 + C2 = (0.03 * 255) ** 2 + + img1 = img1.astype(np.float64) + img2 = img2.astype(np.float64) + kernel = cv2.getGaussianKernel(11, 1.5) + window = np.outer(kernel, kernel.transpose()) + + mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid + mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] + mu1_sq = mu1**2 + mu2_sq = mu2**2 + mu1_mu2 = mu1 * mu2 + sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq + sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq + sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 + + ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ( + (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2) + ) + return ssim_map.mean() + + +""" +# -------------------------------------------- +# matlab's bicubic imresize (numpy and torch) [0, 1] +# -------------------------------------------- +""" + + +# matlab 'imresize' function, now only support 'bicubic' +def cubic(x): + absx = torch.abs(x) + absx2 = absx**2 + absx3 = absx**3 + return (1.5 * absx3 - 2.5 * absx2 + 1) * ((absx <= 1).type_as(absx)) + ( + -0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2 + ) * (((absx > 1) * (absx <= 2)).type_as(absx)) + + +def calculate_weights_indices( + in_length, out_length, scale, kernel, kernel_width, antialiasing +): + if (scale < 1) and (antialiasing): + # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width + kernel_width = kernel_width / scale + + # Output-space coordinates + x = torch.linspace(1, out_length, out_length) + + # Input-space coordinates. Calculate the inverse mapping such that 0.5 + # in output space maps to 0.5 in input space, and 0.5+scale in output + # space maps to 1.5 in input space. + u = x / scale + 0.5 * (1 - 1 / scale) + + # What is the left-most pixel that can be involved in the computation? + left = torch.floor(u - kernel_width / 2) + + # What is the maximum number of pixels that can be involved in the + # computation? Note: it's OK to use an extra pixel here; if the + # corresponding weights are all zero, it will be eliminated at the end + # of this function. + P = math.ceil(kernel_width) + 2 + + # The indices of the input pixels involved in computing the k-th output + # pixel are in row k of the indices matrix. + indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace( + 0, P - 1, P + ).view(1, P).expand(out_length, P) + + # The weights used to compute the k-th output pixel are in row k of the + # weights matrix. + distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices + # apply cubic kernel + if (scale < 1) and (antialiasing): + weights = scale * cubic(distance_to_center * scale) + else: + weights = cubic(distance_to_center) + # Normalize the weights matrix so that each row sums to 1. + weights_sum = torch.sum(weights, 1).view(out_length, 1) + weights = weights / weights_sum.expand(out_length, P) + + # If a column in weights is all zero, get rid of it. only consider the first and last column. + weights_zero_tmp = torch.sum((weights == 0), 0) + if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6): + indices = indices.narrow(1, 1, P - 2) + weights = weights.narrow(1, 1, P - 2) + if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6): + indices = indices.narrow(1, 0, P - 2) + weights = weights.narrow(1, 0, P - 2) + weights = weights.contiguous() + indices = indices.contiguous() + sym_len_s = -indices.min() + 1 + sym_len_e = indices.max() - in_length + indices = indices + sym_len_s - 1 + return weights, indices, int(sym_len_s), int(sym_len_e) + + +# -------------------------------------------- +# imresize for tensor image [0, 1] +# -------------------------------------------- +def imresize(img, scale, antialiasing=True): + # Now the scale should be the same for H and W + # input: img: pytorch tensor, CHW or HW [0,1] + # output: CHW or HW [0,1] w/o round + need_squeeze = True if img.dim() == 2 else False + if need_squeeze: + img.unsqueeze_(0) + in_C, in_H, in_W = img.size() + out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) + kernel_width = 4 + kernel = "cubic" + + # Return the desired dimension order for performing the resize. The + # strategy is to perform the resize first along the dimension with the + # smallest scale factor. + # Now we do not support this. + + # get weights and indices + weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( + in_H, out_H, scale, kernel, kernel_width, antialiasing + ) + weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( + in_W, out_W, scale, kernel, kernel_width, antialiasing + ) + # process H dimension + # symmetric copying + img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W) + img_aug.narrow(1, sym_len_Hs, in_H).copy_(img) + + sym_patch = img[:, :sym_len_Hs, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv) + + sym_patch = img[:, -sym_len_He:, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) + + out_1 = torch.FloatTensor(in_C, out_H, in_W) + kernel_width = weights_H.size(1) + for i in range(out_H): + idx = int(indices_H[i][0]) + for j in range(out_C): + out_1[j, i, :] = ( + img_aug[j, idx : idx + kernel_width, :].transpose(0, 1).mv(weights_H[i]) + ) + + # process W dimension + # symmetric copying + out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We) + out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1) + + sym_patch = out_1[:, :, :sym_len_Ws] + inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(2, inv_idx) + out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv) + + sym_patch = out_1[:, :, -sym_len_We:] + inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(2, inv_idx) + out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) + + out_2 = torch.FloatTensor(in_C, out_H, out_W) + kernel_width = weights_W.size(1) + for i in range(out_W): + idx = int(indices_W[i][0]) + for j in range(out_C): + out_2[j, :, i] = out_1_aug[j, :, idx : idx + kernel_width].mv(weights_W[i]) + if need_squeeze: + out_2.squeeze_() + return out_2 + + +# -------------------------------------------- +# imresize for numpy image [0, 1] +# -------------------------------------------- +def imresize_np(img, scale, antialiasing=True): + # Now the scale should be the same for H and W + # input: img: Numpy, HWC or HW [0,1] + # output: HWC or HW [0,1] w/o round + img = torch.from_numpy(img) + need_squeeze = True if img.dim() == 2 else False + if need_squeeze: + img.unsqueeze_(2) + + in_H, in_W, in_C = img.size() + out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) + kernel_width = 4 + kernel = "cubic" + + # Return the desired dimension order for performing the resize. The + # strategy is to perform the resize first along the dimension with the + # smallest scale factor. + # Now we do not support this. + + # get weights and indices + weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( + in_H, out_H, scale, kernel, kernel_width, antialiasing + ) + weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( + in_W, out_W, scale, kernel, kernel_width, antialiasing + ) + # process H dimension + # symmetric copying + img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C) + img_aug.narrow(0, sym_len_Hs, in_H).copy_(img) + + sym_patch = img[:sym_len_Hs, :, :] + inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(0, inv_idx) + img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv) + + sym_patch = img[-sym_len_He:, :, :] + inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(0, inv_idx) + img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) + + out_1 = torch.FloatTensor(out_H, in_W, in_C) + kernel_width = weights_H.size(1) + for i in range(out_H): + idx = int(indices_H[i][0]) + for j in range(out_C): + out_1[i, :, j] = ( + img_aug[idx : idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i]) + ) + + # process W dimension + # symmetric copying + out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C) + out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1) + + sym_patch = out_1[:, :sym_len_Ws, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv) + + sym_patch = out_1[:, -sym_len_We:, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) + + out_2 = torch.FloatTensor(out_H, out_W, in_C) + kernel_width = weights_W.size(1) + for i in range(out_W): + idx = int(indices_W[i][0]) + for j in range(out_C): + out_2[:, i, j] = out_1_aug[:, idx : idx + kernel_width, j].mv(weights_W[i]) + if need_squeeze: + out_2.squeeze_() + + return out_2.numpy() + + +if __name__ == "__main__": + print("---") +# img = imread_uint('test.bmp', 3) +# img = uint2single(img) +# img_bicubic = imresize_np(img, 1/4) diff --git a/extern/ldm_zero123/modules/losses/__init__.py b/extern/ldm_zero123/modules/losses/__init__.py new file mode 100755 index 0000000..15c99be --- /dev/null +++ b/extern/ldm_zero123/modules/losses/__init__.py @@ -0,0 +1 @@ +from extern.ldm_zero123.modules.losses.contperceptual import LPIPSWithDiscriminator diff --git a/extern/ldm_zero123/modules/losses/contperceptual.py b/extern/ldm_zero123/modules/losses/contperceptual.py new file mode 100755 index 0000000..422f749 --- /dev/null +++ b/extern/ldm_zero123/modules/losses/contperceptual.py @@ -0,0 +1,153 @@ +import torch +import torch.nn as nn +from taming.modules.losses.vqperceptual import * # TODO: taming dependency yes/no? + + +class LPIPSWithDiscriminator(nn.Module): + def __init__( + self, + disc_start, + logvar_init=0.0, + kl_weight=1.0, + pixelloss_weight=1.0, + disc_num_layers=3, + disc_in_channels=3, + disc_factor=1.0, + disc_weight=1.0, + perceptual_weight=1.0, + use_actnorm=False, + disc_conditional=False, + disc_loss="hinge", + ): + super().__init__() + assert disc_loss in ["hinge", "vanilla"] + self.kl_weight = kl_weight + self.pixel_weight = pixelloss_weight + self.perceptual_loss = LPIPS().eval() + self.perceptual_weight = perceptual_weight + # output log variance + self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init) + + self.discriminator = NLayerDiscriminator( + input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm + ).apply(weights_init) + self.discriminator_iter_start = disc_start + self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss + self.disc_factor = disc_factor + self.discriminator_weight = disc_weight + self.disc_conditional = disc_conditional + + def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): + if last_layer is not None: + nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] + g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] + else: + nll_grads = torch.autograd.grad( + nll_loss, self.last_layer[0], retain_graph=True + )[0] + g_grads = torch.autograd.grad( + g_loss, self.last_layer[0], retain_graph=True + )[0] + + d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) + d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() + d_weight = d_weight * self.discriminator_weight + return d_weight + + def forward( + self, + inputs, + reconstructions, + posteriors, + optimizer_idx, + global_step, + last_layer=None, + cond=None, + split="train", + weights=None, + ): + rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) + if self.perceptual_weight > 0: + p_loss = self.perceptual_loss( + inputs.contiguous(), reconstructions.contiguous() + ) + rec_loss = rec_loss + self.perceptual_weight * p_loss + + nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar + weighted_nll_loss = nll_loss + if weights is not None: + weighted_nll_loss = weights * nll_loss + weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0] + nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] + kl_loss = posteriors.kl() + kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] + + # now the GAN part + if optimizer_idx == 0: + # generator update + if cond is None: + assert not self.disc_conditional + logits_fake = self.discriminator(reconstructions.contiguous()) + else: + assert self.disc_conditional + logits_fake = self.discriminator( + torch.cat((reconstructions.contiguous(), cond), dim=1) + ) + g_loss = -torch.mean(logits_fake) + + if self.disc_factor > 0.0: + try: + d_weight = self.calculate_adaptive_weight( + nll_loss, g_loss, last_layer=last_layer + ) + except RuntimeError: + assert not self.training + d_weight = torch.tensor(0.0) + else: + d_weight = torch.tensor(0.0) + + disc_factor = adopt_weight( + self.disc_factor, global_step, threshold=self.discriminator_iter_start + ) + loss = ( + weighted_nll_loss + + self.kl_weight * kl_loss + + d_weight * disc_factor * g_loss + ) + + log = { + "{}/total_loss".format(split): loss.clone().detach().mean(), + "{}/logvar".format(split): self.logvar.detach(), + "{}/kl_loss".format(split): kl_loss.detach().mean(), + "{}/nll_loss".format(split): nll_loss.detach().mean(), + "{}/rec_loss".format(split): rec_loss.detach().mean(), + "{}/d_weight".format(split): d_weight.detach(), + "{}/disc_factor".format(split): torch.tensor(disc_factor), + "{}/g_loss".format(split): g_loss.detach().mean(), + } + return loss, log + + if optimizer_idx == 1: + # second pass for discriminator update + if cond is None: + logits_real = self.discriminator(inputs.contiguous().detach()) + logits_fake = self.discriminator(reconstructions.contiguous().detach()) + else: + logits_real = self.discriminator( + torch.cat((inputs.contiguous().detach(), cond), dim=1) + ) + logits_fake = self.discriminator( + torch.cat((reconstructions.contiguous().detach(), cond), dim=1) + ) + + disc_factor = adopt_weight( + self.disc_factor, global_step, threshold=self.discriminator_iter_start + ) + d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) + + log = { + "{}/disc_loss".format(split): d_loss.clone().detach().mean(), + "{}/logits_real".format(split): logits_real.detach().mean(), + "{}/logits_fake".format(split): logits_fake.detach().mean(), + } + return d_loss, log diff --git a/extern/ldm_zero123/modules/losses/vqperceptual.py b/extern/ldm_zero123/modules/losses/vqperceptual.py new file mode 100755 index 0000000..feb5885 --- /dev/null +++ b/extern/ldm_zero123/modules/losses/vqperceptual.py @@ -0,0 +1,218 @@ +import torch +import torch.nn.functional as F +from einops import repeat +from taming.modules.discriminator.model import NLayerDiscriminator, weights_init +from taming.modules.losses.lpips import LPIPS +from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss +from torch import nn + + +def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights): + assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0] + loss_real = torch.mean(F.relu(1.0 - logits_real), dim=[1, 2, 3]) + loss_fake = torch.mean(F.relu(1.0 + logits_fake), dim=[1, 2, 3]) + loss_real = (weights * loss_real).sum() / weights.sum() + loss_fake = (weights * loss_fake).sum() / weights.sum() + d_loss = 0.5 * (loss_real + loss_fake) + return d_loss + + +def adopt_weight(weight, global_step, threshold=0, value=0.0): + if global_step < threshold: + weight = value + return weight + + +def measure_perplexity(predicted_indices, n_embed): + # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py + # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally + encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed) + avg_probs = encodings.mean(0) + perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp() + cluster_use = torch.sum(avg_probs > 0) + return perplexity, cluster_use + + +def l1(x, y): + return torch.abs(x - y) + + +def l2(x, y): + return torch.pow((x - y), 2) + + +class VQLPIPSWithDiscriminator(nn.Module): + def __init__( + self, + disc_start, + codebook_weight=1.0, + pixelloss_weight=1.0, + disc_num_layers=3, + disc_in_channels=3, + disc_factor=1.0, + disc_weight=1.0, + perceptual_weight=1.0, + use_actnorm=False, + disc_conditional=False, + disc_ndf=64, + disc_loss="hinge", + n_classes=None, + perceptual_loss="lpips", + pixel_loss="l1", + ): + super().__init__() + assert disc_loss in ["hinge", "vanilla"] + assert perceptual_loss in ["lpips", "clips", "dists"] + assert pixel_loss in ["l1", "l2"] + self.codebook_weight = codebook_weight + self.pixel_weight = pixelloss_weight + if perceptual_loss == "lpips": + print(f"{self.__class__.__name__}: Running with LPIPS.") + self.perceptual_loss = LPIPS().eval() + else: + raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<") + self.perceptual_weight = perceptual_weight + + if pixel_loss == "l1": + self.pixel_loss = l1 + else: + self.pixel_loss = l2 + + self.discriminator = NLayerDiscriminator( + input_nc=disc_in_channels, + n_layers=disc_num_layers, + use_actnorm=use_actnorm, + ndf=disc_ndf, + ).apply(weights_init) + self.discriminator_iter_start = disc_start + if disc_loss == "hinge": + self.disc_loss = hinge_d_loss + elif disc_loss == "vanilla": + self.disc_loss = vanilla_d_loss + else: + raise ValueError(f"Unknown GAN loss '{disc_loss}'.") + print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.") + self.disc_factor = disc_factor + self.discriminator_weight = disc_weight + self.disc_conditional = disc_conditional + self.n_classes = n_classes + + def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): + if last_layer is not None: + nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] + g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] + else: + nll_grads = torch.autograd.grad( + nll_loss, self.last_layer[0], retain_graph=True + )[0] + g_grads = torch.autograd.grad( + g_loss, self.last_layer[0], retain_graph=True + )[0] + + d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) + d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() + d_weight = d_weight * self.discriminator_weight + return d_weight + + def forward( + self, + codebook_loss, + inputs, + reconstructions, + optimizer_idx, + global_step, + last_layer=None, + cond=None, + split="train", + predicted_indices=None, + ): + if not exists(codebook_loss): + codebook_loss = torch.tensor([0.0]).to(inputs.device) + # rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) + rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous()) + if self.perceptual_weight > 0: + p_loss = self.perceptual_loss( + inputs.contiguous(), reconstructions.contiguous() + ) + rec_loss = rec_loss + self.perceptual_weight * p_loss + else: + p_loss = torch.tensor([0.0]) + + nll_loss = rec_loss + # nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] + nll_loss = torch.mean(nll_loss) + + # now the GAN part + if optimizer_idx == 0: + # generator update + if cond is None: + assert not self.disc_conditional + logits_fake = self.discriminator(reconstructions.contiguous()) + else: + assert self.disc_conditional + logits_fake = self.discriminator( + torch.cat((reconstructions.contiguous(), cond), dim=1) + ) + g_loss = -torch.mean(logits_fake) + + try: + d_weight = self.calculate_adaptive_weight( + nll_loss, g_loss, last_layer=last_layer + ) + except RuntimeError: + assert not self.training + d_weight = torch.tensor(0.0) + + disc_factor = adopt_weight( + self.disc_factor, global_step, threshold=self.discriminator_iter_start + ) + loss = ( + nll_loss + + d_weight * disc_factor * g_loss + + self.codebook_weight * codebook_loss.mean() + ) + + log = { + "{}/total_loss".format(split): loss.clone().detach().mean(), + "{}/quant_loss".format(split): codebook_loss.detach().mean(), + "{}/nll_loss".format(split): nll_loss.detach().mean(), + "{}/rec_loss".format(split): rec_loss.detach().mean(), + "{}/p_loss".format(split): p_loss.detach().mean(), + "{}/d_weight".format(split): d_weight.detach(), + "{}/disc_factor".format(split): torch.tensor(disc_factor), + "{}/g_loss".format(split): g_loss.detach().mean(), + } + if predicted_indices is not None: + assert self.n_classes is not None + with torch.no_grad(): + perplexity, cluster_usage = measure_perplexity( + predicted_indices, self.n_classes + ) + log[f"{split}/perplexity"] = perplexity + log[f"{split}/cluster_usage"] = cluster_usage + return loss, log + + if optimizer_idx == 1: + # second pass for discriminator update + if cond is None: + logits_real = self.discriminator(inputs.contiguous().detach()) + logits_fake = self.discriminator(reconstructions.contiguous().detach()) + else: + logits_real = self.discriminator( + torch.cat((inputs.contiguous().detach(), cond), dim=1) + ) + logits_fake = self.discriminator( + torch.cat((reconstructions.contiguous().detach(), cond), dim=1) + ) + + disc_factor = adopt_weight( + self.disc_factor, global_step, threshold=self.discriminator_iter_start + ) + d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) + + log = { + "{}/disc_loss".format(split): d_loss.clone().detach().mean(), + "{}/logits_real".format(split): logits_real.detach().mean(), + "{}/logits_fake".format(split): logits_fake.detach().mean(), + } + return d_loss, log diff --git a/extern/ldm_zero123/modules/x_transformer.py b/extern/ldm_zero123/modules/x_transformer.py new file mode 100755 index 0000000..ab8fab2 --- /dev/null +++ b/extern/ldm_zero123/modules/x_transformer.py @@ -0,0 +1,705 @@ +"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers""" +from collections import namedtuple +from functools import partial +from inspect import isfunction + +import torch +import torch.nn.functional as F +from einops import rearrange, reduce, repeat +from torch import einsum, nn + +# constants + +DEFAULT_DIM_HEAD = 64 + +Intermediates = namedtuple("Intermediates", ["pre_softmax_attn", "post_softmax_attn"]) + +LayerIntermediates = namedtuple("Intermediates", ["hiddens", "attn_intermediates"]) + + +class AbsolutePositionalEmbedding(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + self.emb = nn.Embedding(max_seq_len, dim) + self.init_() + + def init_(self): + nn.init.normal_(self.emb.weight, std=0.02) + + def forward(self, x): + n = torch.arange(x.shape[1], device=x.device) + return self.emb(n)[None, :, :] + + +class FixedPositionalEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer("inv_freq", inv_freq) + + def forward(self, x, seq_dim=1, offset=0): + t = ( + torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + + offset + ) + sinusoid_inp = torch.einsum("i , j -> i j", t, self.inv_freq) + emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1) + return emb[None, :, :] + + +# helpers + + +def exists(val): + return val is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def always(val): + def inner(*args, **kwargs): + return val + + return inner + + +def not_equals(val): + def inner(x): + return x != val + + return inner + + +def equals(val): + def inner(x): + return x == val + + return inner + + +def max_neg_value(tensor): + return -torch.finfo(tensor.dtype).max + + +# keyword argument helpers + + +def pick_and_pop(keys, d): + values = list(map(lambda key: d.pop(key), keys)) + return dict(zip(keys, values)) + + +def group_dict_by_key(cond, d): + return_val = [dict(), dict()] + for key in d.keys(): + match = bool(cond(key)) + ind = int(not match) + return_val[ind][key] = d[key] + return (*return_val,) + + +def string_begins_with(prefix, str): + return str.startswith(prefix) + + +def group_by_key_prefix(prefix, d): + return group_dict_by_key(partial(string_begins_with, prefix), d) + + +def groupby_prefix_and_trim(prefix, d): + kwargs_with_prefix, kwargs = group_dict_by_key( + partial(string_begins_with, prefix), d + ) + kwargs_without_prefix = dict( + map(lambda x: (x[0][len(prefix) :], x[1]), tuple(kwargs_with_prefix.items())) + ) + return kwargs_without_prefix, kwargs + + +# classes +class Scale(nn.Module): + def __init__(self, value, fn): + super().__init__() + self.value = value + self.fn = fn + + def forward(self, x, **kwargs): + x, *rest = self.fn(x, **kwargs) + return (x * self.value, *rest) + + +class Rezero(nn.Module): + def __init__(self, fn): + super().__init__() + self.fn = fn + self.g = nn.Parameter(torch.zeros(1)) + + def forward(self, x, **kwargs): + x, *rest = self.fn(x, **kwargs) + return (x * self.g, *rest) + + +class ScaleNorm(nn.Module): + def __init__(self, dim, eps=1e-5): + super().__init__() + self.scale = dim**-0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(1)) + + def forward(self, x): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) * self.g + + +class RMSNorm(nn.Module): + def __init__(self, dim, eps=1e-8): + super().__init__() + self.scale = dim**-0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(dim)) + + def forward(self, x): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) * self.g + + +class Residual(nn.Module): + def forward(self, x, residual): + return x + residual + + +class GRUGating(nn.Module): + def __init__(self, dim): + super().__init__() + self.gru = nn.GRUCell(dim, dim) + + def forward(self, x, residual): + gated_output = self.gru( + rearrange(x, "b n d -> (b n) d"), rearrange(residual, "b n d -> (b n) d") + ) + + return gated_output.reshape_as(x) + + +# feedforward + + +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = ( + nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU()) + if not glu + else GEGLU(dim, inner_dim) + ) + + self.net = nn.Sequential( + project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +# attention. +class Attention(nn.Module): + def __init__( + self, + dim, + dim_head=DEFAULT_DIM_HEAD, + heads=8, + causal=False, + mask=None, + talking_heads=False, + sparse_topk=None, + use_entmax15=False, + num_mem_kv=0, + dropout=0.0, + on_attn=False, + ): + super().__init__() + if use_entmax15: + raise NotImplementedError( + "Check out entmax activation instead of softmax activation!" + ) + self.scale = dim_head**-0.5 + self.heads = heads + self.causal = causal + self.mask = mask + + inner_dim = dim_head * heads + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_k = nn.Linear(dim, inner_dim, bias=False) + self.to_v = nn.Linear(dim, inner_dim, bias=False) + self.dropout = nn.Dropout(dropout) + + # talking heads + self.talking_heads = talking_heads + if talking_heads: + self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads)) + self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads)) + + # explicit topk sparse attention + self.sparse_topk = sparse_topk + + # entmax + # self.attn_fn = entmax15 if use_entmax15 else F.softmax + self.attn_fn = F.softmax + + # add memory key / values + self.num_mem_kv = num_mem_kv + if num_mem_kv > 0: + self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) + self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) + + # attention on attention + self.attn_on_attn = on_attn + self.to_out = ( + nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) + if on_attn + else nn.Linear(inner_dim, dim) + ) + + def forward( + self, + x, + context=None, + mask=None, + context_mask=None, + rel_pos=None, + sinusoidal_emb=None, + prev_attn=None, + mem=None, + ): + b, n, _, h, talking_heads, device = ( + *x.shape, + self.heads, + self.talking_heads, + x.device, + ) + kv_input = default(context, x) + + q_input = x + k_input = kv_input + v_input = kv_input + + if exists(mem): + k_input = torch.cat((mem, k_input), dim=-2) + v_input = torch.cat((mem, v_input), dim=-2) + + if exists(sinusoidal_emb): + # in shortformer, the query would start at a position offset depending on the past cached memory + offset = k_input.shape[-2] - q_input.shape[-2] + q_input = q_input + sinusoidal_emb(q_input, offset=offset) + k_input = k_input + sinusoidal_emb(k_input) + + q = self.to_q(q_input) + k = self.to_k(k_input) + v = self.to_v(v_input) + + q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v)) + + input_mask = None + if any(map(exists, (mask, context_mask))): + q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool()) + k_mask = q_mask if not exists(context) else context_mask + k_mask = default( + k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool() + ) + q_mask = rearrange(q_mask, "b i -> b () i ()") + k_mask = rearrange(k_mask, "b j -> b () () j") + input_mask = q_mask * k_mask + + if self.num_mem_kv > 0: + mem_k, mem_v = map( + lambda t: repeat(t, "h n d -> b h n d", b=b), (self.mem_k, self.mem_v) + ) + k = torch.cat((mem_k, k), dim=-2) + v = torch.cat((mem_v, v), dim=-2) + if exists(input_mask): + input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True) + + dots = einsum("b h i d, b h j d -> b h i j", q, k) * self.scale + mask_value = max_neg_value(dots) + + if exists(prev_attn): + dots = dots + prev_attn + + pre_softmax_attn = dots + + if talking_heads: + dots = einsum( + "b h i j, h k -> b k i j", dots, self.pre_softmax_proj + ).contiguous() + + if exists(rel_pos): + dots = rel_pos(dots) + + if exists(input_mask): + dots.masked_fill_(~input_mask, mask_value) + del input_mask + + if self.causal: + i, j = dots.shape[-2:] + r = torch.arange(i, device=device) + mask = rearrange(r, "i -> () () i ()") < rearrange(r, "j -> () () () j") + mask = F.pad(mask, (j - i, 0), value=False) + dots.masked_fill_(mask, mask_value) + del mask + + if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]: + top, _ = dots.topk(self.sparse_topk, dim=-1) + vk = top[..., -1].unsqueeze(-1).expand_as(dots) + mask = dots < vk + dots.masked_fill_(mask, mask_value) + del mask + + attn = self.attn_fn(dots, dim=-1) + post_softmax_attn = attn + + attn = self.dropout(attn) + + if talking_heads: + attn = einsum( + "b h i j, h k -> b k i j", attn, self.post_softmax_proj + ).contiguous() + + out = einsum("b h i j, b h j d -> b h i d", attn, v) + out = rearrange(out, "b h n d -> b n (h d)") + + intermediates = Intermediates( + pre_softmax_attn=pre_softmax_attn, post_softmax_attn=post_softmax_attn + ) + + return self.to_out(out), intermediates + + +class AttentionLayers(nn.Module): + def __init__( + self, + dim, + depth, + heads=8, + causal=False, + cross_attend=False, + only_cross=False, + use_scalenorm=False, + use_rmsnorm=False, + use_rezero=False, + rel_pos_num_buckets=32, + rel_pos_max_distance=128, + position_infused_attn=False, + custom_layers=None, + sandwich_coef=None, + par_ratio=None, + residual_attn=False, + cross_residual_attn=False, + macaron=False, + pre_norm=True, + gate_residual=False, + **kwargs, + ): + super().__init__() + ff_kwargs, kwargs = groupby_prefix_and_trim("ff_", kwargs) + attn_kwargs, _ = groupby_prefix_and_trim("attn_", kwargs) + + dim_head = attn_kwargs.get("dim_head", DEFAULT_DIM_HEAD) + + self.dim = dim + self.depth = depth + self.layers = nn.ModuleList([]) + + self.has_pos_emb = position_infused_attn + self.pia_pos_emb = ( + FixedPositionalEmbedding(dim) if position_infused_attn else None + ) + self.rotary_pos_emb = always(None) + + assert ( + rel_pos_num_buckets <= rel_pos_max_distance + ), "number of relative position buckets must be less than the relative position max distance" + self.rel_pos = None + + self.pre_norm = pre_norm + + self.residual_attn = residual_attn + self.cross_residual_attn = cross_residual_attn + + norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm + norm_class = RMSNorm if use_rmsnorm else norm_class + norm_fn = partial(norm_class, dim) + + norm_fn = nn.Identity if use_rezero else norm_fn + branch_fn = Rezero if use_rezero else None + + if cross_attend and not only_cross: + default_block = ("a", "c", "f") + elif cross_attend and only_cross: + default_block = ("c", "f") + else: + default_block = ("a", "f") + + if macaron: + default_block = ("f",) + default_block + + if exists(custom_layers): + layer_types = custom_layers + elif exists(par_ratio): + par_depth = depth * len(default_block) + assert 1 < par_ratio <= par_depth, "par ratio out of range" + default_block = tuple(filter(not_equals("f"), default_block)) + par_attn = par_depth // par_ratio + depth_cut = ( + par_depth * 2 // 3 + ) # 2 / 3 attention layer cutoff suggested by PAR paper + par_width = (depth_cut + depth_cut // par_attn) // par_attn + assert ( + len(default_block) <= par_width + ), "default block is too large for par_ratio" + par_block = default_block + ("f",) * (par_width - len(default_block)) + par_head = par_block * par_attn + layer_types = par_head + ("f",) * (par_depth - len(par_head)) + elif exists(sandwich_coef): + assert ( + sandwich_coef > 0 and sandwich_coef <= depth + ), "sandwich coefficient should be less than the depth" + layer_types = ( + ("a",) * sandwich_coef + + default_block * (depth - sandwich_coef) + + ("f",) * sandwich_coef + ) + else: + layer_types = default_block * depth + + self.layer_types = layer_types + self.num_attn_layers = len(list(filter(equals("a"), layer_types))) + + for layer_type in self.layer_types: + if layer_type == "a": + layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs) + elif layer_type == "c": + layer = Attention(dim, heads=heads, **attn_kwargs) + elif layer_type == "f": + layer = FeedForward(dim, **ff_kwargs) + layer = layer if not macaron else Scale(0.5, layer) + else: + raise Exception(f"invalid layer type {layer_type}") + + if isinstance(layer, Attention) and exists(branch_fn): + layer = branch_fn(layer) + + if gate_residual: + residual_fn = GRUGating(dim) + else: + residual_fn = Residual() + + self.layers.append(nn.ModuleList([norm_fn(), layer, residual_fn])) + + def forward( + self, + x, + context=None, + mask=None, + context_mask=None, + mems=None, + return_hiddens=False, + ): + hiddens = [] + intermediates = [] + prev_attn = None + prev_cross_attn = None + + mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers + + for ind, (layer_type, (norm, block, residual_fn)) in enumerate( + zip(self.layer_types, self.layers) + ): + is_last = ind == (len(self.layers) - 1) + + if layer_type == "a": + hiddens.append(x) + layer_mem = mems.pop(0) + + residual = x + + if self.pre_norm: + x = norm(x) + + if layer_type == "a": + out, inter = block( + x, + mask=mask, + sinusoidal_emb=self.pia_pos_emb, + rel_pos=self.rel_pos, + prev_attn=prev_attn, + mem=layer_mem, + ) + elif layer_type == "c": + out, inter = block( + x, + context=context, + mask=mask, + context_mask=context_mask, + prev_attn=prev_cross_attn, + ) + elif layer_type == "f": + out = block(x) + + x = residual_fn(out, residual) + + if layer_type in ("a", "c"): + intermediates.append(inter) + + if layer_type == "a" and self.residual_attn: + prev_attn = inter.pre_softmax_attn + elif layer_type == "c" and self.cross_residual_attn: + prev_cross_attn = inter.pre_softmax_attn + + if not self.pre_norm and not is_last: + x = norm(x) + + if return_hiddens: + intermediates = LayerIntermediates( + hiddens=hiddens, attn_intermediates=intermediates + ) + + return x, intermediates + + return x + + +class Encoder(AttentionLayers): + def __init__(self, **kwargs): + assert "causal" not in kwargs, "cannot set causality on encoder" + super().__init__(causal=False, **kwargs) + + +class TransformerWrapper(nn.Module): + def __init__( + self, + *, + num_tokens, + max_seq_len, + attn_layers, + emb_dim=None, + max_mem_len=0.0, + emb_dropout=0.0, + num_memory_tokens=None, + tie_embedding=False, + use_pos_emb=True, + ): + super().__init__() + assert isinstance( + attn_layers, AttentionLayers + ), "attention layers must be one of Encoder or Decoder" + + dim = attn_layers.dim + emb_dim = default(emb_dim, dim) + + self.max_seq_len = max_seq_len + self.max_mem_len = max_mem_len + self.num_tokens = num_tokens + + self.token_emb = nn.Embedding(num_tokens, emb_dim) + self.pos_emb = ( + AbsolutePositionalEmbedding(emb_dim, max_seq_len) + if (use_pos_emb and not attn_layers.has_pos_emb) + else always(0) + ) + self.emb_dropout = nn.Dropout(emb_dropout) + + self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity() + self.attn_layers = attn_layers + self.norm = nn.LayerNorm(dim) + + self.init_() + + self.to_logits = ( + nn.Linear(dim, num_tokens) + if not tie_embedding + else lambda t: t @ self.token_emb.weight.t() + ) + + # memory tokens (like [cls]) from Memory Transformers paper + num_memory_tokens = default(num_memory_tokens, 0) + self.num_memory_tokens = num_memory_tokens + if num_memory_tokens > 0: + self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim)) + + # let funnel encoder know number of memory tokens, if specified + if hasattr(attn_layers, "num_memory_tokens"): + attn_layers.num_memory_tokens = num_memory_tokens + + def init_(self): + nn.init.normal_(self.token_emb.weight, std=0.02) + + def forward( + self, + x, + return_embeddings=False, + mask=None, + return_mems=False, + return_attn=False, + mems=None, + **kwargs, + ): + b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens + x = self.token_emb(x) + x += self.pos_emb(x) + x = self.emb_dropout(x) + + x = self.project_emb(x) + + if num_mem > 0: + mem = repeat(self.memory_tokens, "n d -> b n d", b=b) + x = torch.cat((mem, x), dim=1) + + # auto-handle masking after appending memory tokens + if exists(mask): + mask = F.pad(mask, (num_mem, 0), value=True) + + x, intermediates = self.attn_layers( + x, mask=mask, mems=mems, return_hiddens=True, **kwargs + ) + x = self.norm(x) + + mem, x = x[:, :num_mem], x[:, num_mem:] + + out = self.to_logits(x) if not return_embeddings else x + + if return_mems: + hiddens = intermediates.hiddens + new_mems = ( + list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) + if exists(mems) + else hiddens + ) + new_mems = list( + map(lambda t: t[..., -self.max_mem_len :, :].detach(), new_mems) + ) + return out, new_mems + + if return_attn: + attn_maps = list( + map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates) + ) + return out, attn_maps + + return out diff --git a/extern/ldm_zero123/thirdp/psp/helpers.py b/extern/ldm_zero123/thirdp/psp/helpers.py new file mode 100755 index 0000000..fc0de4d --- /dev/null +++ b/extern/ldm_zero123/thirdp/psp/helpers.py @@ -0,0 +1,144 @@ +# https://github.com/eladrich/pixel2style2pixel + +from collections import namedtuple + +import torch +from torch.nn import ( + AdaptiveAvgPool2d, + BatchNorm2d, + Conv2d, + MaxPool2d, + Module, + PReLU, + ReLU, + Sequential, + Sigmoid, +) + +""" +ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) +""" + + +class Flatten(Module): + def forward(self, input): + return input.view(input.size(0), -1) + + +def l2_norm(input, axis=1): + norm = torch.norm(input, 2, axis, True) + output = torch.div(input, norm) + return output + + +class Bottleneck(namedtuple("Block", ["in_channel", "depth", "stride"])): + """A named tuple describing a ResNet block.""" + + +def get_block(in_channel, depth, num_units, stride=2): + return [Bottleneck(in_channel, depth, stride)] + [ + Bottleneck(depth, depth, 1) for i in range(num_units - 1) + ] + + +def get_blocks(num_layers): + if num_layers == 50: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=4), + get_block(in_channel=128, depth=256, num_units=14), + get_block(in_channel=256, depth=512, num_units=3), + ] + elif num_layers == 100: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=13), + get_block(in_channel=128, depth=256, num_units=30), + get_block(in_channel=256, depth=512, num_units=3), + ] + elif num_layers == 152: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=8), + get_block(in_channel=128, depth=256, num_units=36), + get_block(in_channel=256, depth=512, num_units=3), + ] + else: + raise ValueError( + "Invalid number of layers: {}. Must be one of [50, 100, 152]".format( + num_layers + ) + ) + return blocks + + +class SEModule(Module): + def __init__(self, channels, reduction): + super(SEModule, self).__init__() + self.avg_pool = AdaptiveAvgPool2d(1) + self.fc1 = Conv2d( + channels, channels // reduction, kernel_size=1, padding=0, bias=False + ) + self.relu = ReLU(inplace=True) + self.fc2 = Conv2d( + channels // reduction, channels, kernel_size=1, padding=0, bias=False + ) + self.sigmoid = Sigmoid() + + def forward(self, x): + module_input = x + x = self.avg_pool(x) + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + x = self.sigmoid(x) + return module_input * x + + +class bottleneck_IR(Module): + def __init__(self, in_channel, depth, stride): + super(bottleneck_IR, self).__init__() + if in_channel == depth: + self.shortcut_layer = MaxPool2d(1, stride) + else: + self.shortcut_layer = Sequential( + Conv2d(in_channel, depth, (1, 1), stride, bias=False), + BatchNorm2d(depth), + ) + self.res_layer = Sequential( + BatchNorm2d(in_channel), + Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), + PReLU(depth), + Conv2d(depth, depth, (3, 3), stride, 1, bias=False), + BatchNorm2d(depth), + ) + + def forward(self, x): + shortcut = self.shortcut_layer(x) + res = self.res_layer(x) + return res + shortcut + + +class bottleneck_IR_SE(Module): + def __init__(self, in_channel, depth, stride): + super(bottleneck_IR_SE, self).__init__() + if in_channel == depth: + self.shortcut_layer = MaxPool2d(1, stride) + else: + self.shortcut_layer = Sequential( + Conv2d(in_channel, depth, (1, 1), stride, bias=False), + BatchNorm2d(depth), + ) + self.res_layer = Sequential( + BatchNorm2d(in_channel), + Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), + PReLU(depth), + Conv2d(depth, depth, (3, 3), stride, 1, bias=False), + BatchNorm2d(depth), + SEModule(depth, 16), + ) + + def forward(self, x): + shortcut = self.shortcut_layer(x) + res = self.res_layer(x) + return res + shortcut diff --git a/extern/ldm_zero123/thirdp/psp/id_loss.py b/extern/ldm_zero123/thirdp/psp/id_loss.py new file mode 100755 index 0000000..76ab9eb --- /dev/null +++ b/extern/ldm_zero123/thirdp/psp/id_loss.py @@ -0,0 +1,26 @@ +# https://github.com/eladrich/pixel2style2pixel +import torch +from torch import nn + +from extern.ldm_zero123.thirdp.psp.model_irse import Backbone + + +class IDFeatures(nn.Module): + def __init__(self, model_path): + super(IDFeatures, self).__init__() + print("Loading ResNet ArcFace") + self.facenet = Backbone( + input_size=112, num_layers=50, drop_ratio=0.6, mode="ir_se" + ) + self.facenet.load_state_dict(torch.load(model_path, map_location="cpu")) + self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112)) + self.facenet.eval() + + def forward(self, x, crop=False): + # Not sure of the image range here + if crop: + x = torch.nn.functional.interpolate(x, (256, 256), mode="area") + x = x[:, :, 35:223, 32:220] + x = self.face_pool(x) + x_feats = self.facenet(x) + return x_feats diff --git a/extern/ldm_zero123/thirdp/psp/model_irse.py b/extern/ldm_zero123/thirdp/psp/model_irse.py new file mode 100755 index 0000000..50174ab --- /dev/null +++ b/extern/ldm_zero123/thirdp/psp/model_irse.py @@ -0,0 +1,118 @@ +# https://github.com/eladrich/pixel2style2pixel + +from torch.nn import ( + BatchNorm1d, + BatchNorm2d, + Conv2d, + Dropout, + Linear, + Module, + PReLU, + Sequential, +) + +from extern.ldm_zero123.thirdp.psp.helpers import ( + Flatten, + bottleneck_IR, + bottleneck_IR_SE, + get_blocks, + l2_norm, +) + +""" +Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) +""" + + +class Backbone(Module): + def __init__(self, input_size, num_layers, mode="ir", drop_ratio=0.4, affine=True): + super(Backbone, self).__init__() + assert input_size in [112, 224], "input_size should be 112 or 224" + assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152" + assert mode in ["ir", "ir_se"], "mode should be ir or ir_se" + blocks = get_blocks(num_layers) + if mode == "ir": + unit_module = bottleneck_IR + elif mode == "ir_se": + unit_module = bottleneck_IR_SE + self.input_layer = Sequential( + Conv2d(3, 64, (3, 3), 1, 1, bias=False), BatchNorm2d(64), PReLU(64) + ) + if input_size == 112: + self.output_layer = Sequential( + BatchNorm2d(512), + Dropout(drop_ratio), + Flatten(), + Linear(512 * 7 * 7, 512), + BatchNorm1d(512, affine=affine), + ) + else: + self.output_layer = Sequential( + BatchNorm2d(512), + Dropout(drop_ratio), + Flatten(), + Linear(512 * 14 * 14, 512), + BatchNorm1d(512, affine=affine), + ) + + modules = [] + for block in blocks: + for bottleneck in block: + modules.append( + unit_module( + bottleneck.in_channel, bottleneck.depth, bottleneck.stride + ) + ) + self.body = Sequential(*modules) + + def forward(self, x): + x = self.input_layer(x) + x = self.body(x) + x = self.output_layer(x) + return l2_norm(x) + + +def IR_50(input_size): + """Constructs a ir-50 model.""" + model = Backbone(input_size, num_layers=50, mode="ir", drop_ratio=0.4, affine=False) + return model + + +def IR_101(input_size): + """Constructs a ir-101 model.""" + model = Backbone( + input_size, num_layers=100, mode="ir", drop_ratio=0.4, affine=False + ) + return model + + +def IR_152(input_size): + """Constructs a ir-152 model.""" + model = Backbone( + input_size, num_layers=152, mode="ir", drop_ratio=0.4, affine=False + ) + return model + + +def IR_SE_50(input_size): + """Constructs a ir_se-50 model.""" + model = Backbone( + input_size, num_layers=50, mode="ir_se", drop_ratio=0.4, affine=False + ) + return model + + +def IR_SE_101(input_size): + """Constructs a ir_se-101 model.""" + model = Backbone( + input_size, num_layers=100, mode="ir_se", drop_ratio=0.4, affine=False + ) + return model + + +def IR_SE_152(input_size): + """Constructs a ir_se-152 model.""" + model = Backbone( + input_size, num_layers=152, mode="ir_se", drop_ratio=0.4, affine=False + ) + return model diff --git a/extern/ldm_zero123/util.py b/extern/ldm_zero123/util.py new file mode 100755 index 0000000..4664798 --- /dev/null +++ b/extern/ldm_zero123/util.py @@ -0,0 +1,249 @@ +import importlib +import os +import time +from inspect import isfunction + +import cv2 +import matplotlib.pyplot as plt +import numpy as np +import PIL +import torch +import torchvision +from PIL import Image, ImageDraw, ImageFont +from torch import optim + + +def pil_rectangle_crop(im): + width, height = im.size # Get dimensions + + if width <= height: + left = 0 + right = width + top = (height - width) / 2 + bottom = (height + width) / 2 + else: + top = 0 + bottom = height + left = (width - height) / 2 + bottom = (width + height) / 2 + + # Crop the center of the image + im = im.crop((left, top, right, bottom)) + return im + + +def log_txt_as_img(wh, xc, size=10): + # wh a tuple of (width, height) + # xc a list of captions to plot + b = len(xc) + txts = list() + for bi in range(b): + txt = Image.new("RGB", wh, color="white") + draw = ImageDraw.Draw(txt) + font = ImageFont.truetype("data/DejaVuSans.ttf", size=size) + nc = int(40 * (wh[0] / 256)) + lines = "\n".join( + xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc) + ) + + try: + draw.text((0, 0), lines, fill="black", font=font) + except UnicodeEncodeError: + print("Cant encode string for logging. Skipping.") + + txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 + txts.append(txt) + txts = np.stack(txts) + txts = torch.tensor(txts) + return txts + + +def ismap(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] > 3) + + +def isimage(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) + + +def exists(x): + return x is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def mean_flat(tensor): + """ + https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def count_params(model, verbose=False): + total_params = sum(p.numel() for p in model.parameters()) + if verbose: + print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") + return total_params + + +def instantiate_from_config(config): + if not "target" in config: + if config == "__is_first_stage__": + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +class AdamWwithEMAandWings(optim.Optimizer): + # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298 + def __init__( + self, + params, + lr=1.0e-3, + betas=(0.9, 0.999), + eps=1.0e-8, # TODO: check hyperparameters before using + weight_decay=1.0e-2, + amsgrad=False, + ema_decay=0.9999, # ema decay to match previous code + ema_power=1.0, + param_names=(), + ): + """AdamW that saves EMA versions of the parameters.""" + if not 0.0 <= lr: + raise ValueError("Invalid learning rate: {}".format(lr)) + if not 0.0 <= eps: + raise ValueError("Invalid epsilon value: {}".format(eps)) + if not 0.0 <= betas[0] < 1.0: + raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) + if not 0.0 <= betas[1] < 1.0: + raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) + if not 0.0 <= weight_decay: + raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) + if not 0.0 <= ema_decay <= 1.0: + raise ValueError("Invalid ema_decay value: {}".format(ema_decay)) + defaults = dict( + lr=lr, + betas=betas, + eps=eps, + weight_decay=weight_decay, + amsgrad=amsgrad, + ema_decay=ema_decay, + ema_power=ema_power, + param_names=param_names, + ) + super().__init__(params, defaults) + + def __setstate__(self, state): + super().__setstate__(state) + for group in self.param_groups: + group.setdefault("amsgrad", False) + + @torch.no_grad() + def step(self, closure=None): + """Performs a single optimization step. + Args: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + for group in self.param_groups: + params_with_grad = [] + grads = [] + exp_avgs = [] + exp_avg_sqs = [] + ema_params_with_grad = [] + state_sums = [] + max_exp_avg_sqs = [] + state_steps = [] + amsgrad = group["amsgrad"] + beta1, beta2 = group["betas"] + ema_decay = group["ema_decay"] + ema_power = group["ema_power"] + + for p in group["params"]: + if p.grad is None: + continue + params_with_grad.append(p) + if p.grad.is_sparse: + raise RuntimeError("AdamW does not support sparse gradients") + grads.append(p.grad) + + state = self.state[p] + + # State initialization + if len(state) == 0: + state["step"] = 0 + # Exponential moving average of gradient values + state["exp_avg"] = torch.zeros_like( + p, memory_format=torch.preserve_format + ) + # Exponential moving average of squared gradient values + state["exp_avg_sq"] = torch.zeros_like( + p, memory_format=torch.preserve_format + ) + if amsgrad: + # Maintains max of all exp. moving avg. of sq. grad. values + state["max_exp_avg_sq"] = torch.zeros_like( + p, memory_format=torch.preserve_format + ) + # Exponential moving average of parameter values + state["param_exp_avg"] = p.detach().float().clone() + + exp_avgs.append(state["exp_avg"]) + exp_avg_sqs.append(state["exp_avg_sq"]) + ema_params_with_grad.append(state["param_exp_avg"]) + + if amsgrad: + max_exp_avg_sqs.append(state["max_exp_avg_sq"]) + + # update the steps for each param group update + state["step"] += 1 + # record the step after step update + state_steps.append(state["step"]) + + optim._functional.adamw( + params_with_grad, + grads, + exp_avgs, + exp_avg_sqs, + max_exp_avg_sqs, + state_steps, + amsgrad=amsgrad, + beta1=beta1, + beta2=beta2, + lr=group["lr"], + weight_decay=group["weight_decay"], + eps=group["eps"], + maximize=False, + ) + + cur_ema_decay = min(ema_decay, 1 - state["step"] ** -ema_power) + for param, ema_param in zip(params_with_grad, ema_params_with_grad): + ema_param.mul_(cur_ema_decay).add_( + param.float(), alpha=1 - cur_ema_decay + ) + + return loss diff --git a/extern/zero123.py b/extern/zero123.py new file mode 100644 index 0000000..2ee4343 --- /dev/null +++ b/extern/zero123.py @@ -0,0 +1,666 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import inspect +import math +import warnings +from typing import Any, Callable, Dict, List, Optional, Union + +import PIL +import torch +import torchvision.transforms.functional as TF +from diffusers.configuration_utils import ConfigMixin, FrozenDict, register_to_config +from diffusers.image_processor import VaeImageProcessor +from diffusers.models import AutoencoderKL, UNet2DConditionModel +from diffusers.models.modeling_utils import ModelMixin +from diffusers.pipelines.pipeline_utils import DiffusionPipeline +from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput +from diffusers.pipelines.stable_diffusion.safety_checker import ( + StableDiffusionSafetyChecker, +) +from diffusers.schedulers import KarrasDiffusionSchedulers +from diffusers.utils import deprecate, is_accelerate_available, logging +from diffusers.utils.torch_utils import randn_tensor +from packaging import version +from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +class CLIPCameraProjection(ModelMixin, ConfigMixin): + """ + A Projection layer for CLIP embedding and camera embedding. + + Parameters: + embedding_dim (`int`, *optional*, defaults to 768): The dimension of the model input `clip_embed` + additional_embeddings (`int`, *optional*, defaults to 4): The number of additional tokens appended to the + projected `hidden_states`. The actual length of the used `hidden_states` is `num_embeddings + + additional_embeddings`. + """ + + @register_to_config + def __init__(self, embedding_dim: int = 768, additional_embeddings: int = 4): + super().__init__() + self.embedding_dim = embedding_dim + self.additional_embeddings = additional_embeddings + + self.input_dim = self.embedding_dim + self.additional_embeddings + self.output_dim = self.embedding_dim + + self.proj = torch.nn.Linear(self.input_dim, self.output_dim) + + def forward( + self, + embedding: torch.FloatTensor, + ): + """ + The [`PriorTransformer`] forward method. + + Args: + hidden_states (`torch.FloatTensor` of shape `(batch_size, input_dim)`): + The currently input embeddings. + + Returns: + The output embedding projection (`torch.FloatTensor` of shape `(batch_size, output_dim)`). + """ + proj_embedding = self.proj(embedding) + return proj_embedding + + +class Zero123Pipeline(DiffusionPipeline): + r""" + Pipeline to generate variations from an input image using Stable Diffusion. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the + library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + image_encoder ([`CLIPVisionModelWithProjection`]): + Frozen CLIP image-encoder. Stable Diffusion Image Variation uses the vision portion of + [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModelWithProjection), + specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. + unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of + [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. + safety_checker ([`StableDiffusionSafetyChecker`]): + Classification module that estimates whether generated images could be considered offensive or harmful. + Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. + feature_extractor ([`CLIPImageProcessor`]): + Model that extracts features from generated images to be used as inputs for the `safety_checker`. + """ + # TODO: feature_extractor is required to encode images (if they are in PIL format), + # we should give a descriptive message if the pipeline doesn't have one. + _optional_components = ["safety_checker"] + + def __init__( + self, + vae: AutoencoderKL, + image_encoder: CLIPVisionModelWithProjection, + unet: UNet2DConditionModel, + scheduler: KarrasDiffusionSchedulers, + safety_checker: StableDiffusionSafetyChecker, + feature_extractor: CLIPImageProcessor, + clip_camera_projection: CLIPCameraProjection, + requires_safety_checker: bool = True, + ): + super().__init__() + + if safety_checker is None and requires_safety_checker: + logger.warn( + f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" + " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" + " results in services or applications open to the public. Both the diffusers team and Hugging Face" + " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" + " it only for use-cases that involve analyzing network behavior or auditing its results. For more" + " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." + ) + + if safety_checker is not None and feature_extractor is None: + raise ValueError( + "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" + " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." + ) + + is_unet_version_less_0_9_0 = hasattr( + unet.config, "_diffusers_version" + ) and version.parse( + version.parse(unet.config._diffusers_version).base_version + ) < version.parse( + "0.9.0.dev0" + ) + is_unet_sample_size_less_64 = ( + hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 + ) + if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: + deprecation_message = ( + "The configuration file of the unet has set the default `sample_size` to smaller than" + " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" + " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" + " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" + " in the config might lead to incorrect results in future versions. If you have downloaded this" + " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" + " the `unet/config.json` file" + ) + deprecate( + "sample_size<64", "1.0.0", deprecation_message, standard_warn=False + ) + new_config = dict(unet.config) + new_config["sample_size"] = 64 + unet._internal_dict = FrozenDict(new_config) + + self.register_modules( + vae=vae, + image_encoder=image_encoder, + unet=unet, + scheduler=scheduler, + safety_checker=safety_checker, + feature_extractor=feature_extractor, + clip_camera_projection=clip_camera_projection, + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + self.register_to_config(requires_safety_checker=requires_safety_checker) + + def enable_sequential_cpu_offload(self, gpu_id=0): + r""" + Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, + text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a + `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. + """ + if is_accelerate_available(): + from accelerate import cpu_offload + else: + raise ImportError("Please install accelerate via `pip install accelerate`") + + device = torch.device(f"cuda:{gpu_id}") + + for cpu_offloaded_model in [ + self.unet, + self.image_encoder, + self.vae, + self.safety_checker, + ]: + if cpu_offloaded_model is not None: + cpu_offload(cpu_offloaded_model, device) + + @property + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device + def _execution_device(self): + r""" + Returns the device on which the pipeline's models will be executed. After calling + `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module + hooks. + """ + if not hasattr(self.unet, "_hf_hook"): + return self.device + for module in self.unet.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + def _encode_image( + self, + image, + elevation, + azimuth, + distance, + device, + num_images_per_prompt, + do_classifier_free_guidance, + clip_image_embeddings=None, + image_camera_embeddings=None, + ): + dtype = next(self.image_encoder.parameters()).dtype + + if image_camera_embeddings is None: + if image is None: + assert clip_image_embeddings is not None + image_embeddings = clip_image_embeddings.to(device=device, dtype=dtype) + else: + if not isinstance(image, torch.Tensor): + image = self.feature_extractor( + images=image, return_tensors="pt" + ).pixel_values + + image = image.to(device=device, dtype=dtype) + image_embeddings = self.image_encoder(image).image_embeds + image_embeddings = image_embeddings.unsqueeze(1) + + bs_embed, seq_len, _ = image_embeddings.shape + + if isinstance(elevation, float): + elevation = torch.as_tensor( + [elevation] * bs_embed, dtype=dtype, device=device + ) + if isinstance(azimuth, float): + azimuth = torch.as_tensor( + [azimuth] * bs_embed, dtype=dtype, device=device + ) + if isinstance(distance, float): + distance = torch.as_tensor( + [distance] * bs_embed, dtype=dtype, device=device + ) + + camera_embeddings = torch.stack( + [ + torch.deg2rad(elevation), + torch.sin(torch.deg2rad(azimuth)), + torch.cos(torch.deg2rad(azimuth)), + distance, + ], + dim=-1, + )[:, None, :] + + image_embeddings = torch.cat([image_embeddings, camera_embeddings], dim=-1) + + # project (image, camera) embeddings to the same dimension as clip embeddings + image_embeddings = self.clip_camera_projection(image_embeddings) + else: + image_embeddings = image_camera_embeddings.to(device=device, dtype=dtype) + bs_embed, seq_len, _ = image_embeddings.shape + + # duplicate image embeddings for each generation per prompt, using mps friendly method + image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1) + image_embeddings = image_embeddings.view( + bs_embed * num_images_per_prompt, seq_len, -1 + ) + + if do_classifier_free_guidance: + negative_prompt_embeds = torch.zeros_like(image_embeddings) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings]) + + return image_embeddings + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker + def run_safety_checker(self, image, device, dtype): + if self.safety_checker is None: + has_nsfw_concept = None + else: + if torch.is_tensor(image): + feature_extractor_input = self.image_processor.postprocess( + image, output_type="pil" + ) + else: + feature_extractor_input = self.image_processor.numpy_to_pil(image) + safety_checker_input = self.feature_extractor( + feature_extractor_input, return_tensors="pt" + ).to(device) + image, has_nsfw_concept = self.safety_checker( + images=image, clip_input=safety_checker_input.pixel_values.to(dtype) + ) + return image, has_nsfw_concept + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents + def decode_latents(self, latents): + warnings.warn( + "The decode_latents method is deprecated and will be removed in a future version. Please" + " use VaeImageProcessor instead", + FutureWarning, + ) + latents = 1 / self.vae.config.scaling_factor * latents + image = self.vae.decode(latents, return_dict=False)[0] + image = (image / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 + image = image.cpu().permute(0, 2, 3, 1).float().numpy() + return image + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set( + inspect.signature(self.scheduler.step).parameters.keys() + ) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set( + inspect.signature(self.scheduler.step).parameters.keys() + ) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs(self, image, height, width, callback_steps): + # TODO: check image size or adjust image size to (height, width) + + if height % 8 != 0 or width % 8 != 0: + raise ValueError( + f"`height` and `width` have to be divisible by 8 but are {height} and {width}." + ) + + if (callback_steps is None) or ( + callback_steps is not None + and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents + def prepare_latents( + self, + batch_size, + num_channels_latents, + height, + width, + dtype, + device, + generator, + latents=None, + ): + shape = ( + batch_size, + num_channels_latents, + height // self.vae_scale_factor, + width // self.vae_scale_factor, + ) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor( + shape, generator=generator, device=device, dtype=dtype + ) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def _get_latent_model_input( + self, + latents: torch.FloatTensor, + image: Optional[ + Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor] + ], + num_images_per_prompt: int, + do_classifier_free_guidance: bool, + image_latents: Optional[torch.FloatTensor] = None, + ): + if isinstance(image, PIL.Image.Image): + image_pt = TF.to_tensor(image).unsqueeze(0).to(latents) + elif isinstance(image, list): + image_pt = torch.stack([TF.to_tensor(img) for img in image], dim=0).to( + latents + ) + elif isinstance(image, torch.Tensor): + image_pt = image + else: + image_pt = None + + if image_pt is None: + assert image_latents is not None + image_pt = image_latents.repeat_interleave(num_images_per_prompt, dim=0) + else: + image_pt = image_pt * 2.0 - 1.0 # scale to [-1, 1] + # FIXME: encoded latents should be multiplied with self.vae.config.scaling_factor + # but zero123 was not trained this way + image_pt = self.vae.encode(image_pt).latent_dist.mode() + image_pt = image_pt.repeat_interleave(num_images_per_prompt, dim=0) + if do_classifier_free_guidance: + latent_model_input = torch.cat( + [ + torch.cat([latents, latents], dim=0), + torch.cat([torch.zeros_like(image_pt), image_pt], dim=0), + ], + dim=1, + ) + else: + latent_model_input = torch.cat([latents, image_pt], dim=1) + + return latent_model_input + + @torch.no_grad() + def __call__( + self, + image: Optional[ + Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor] + ] = None, + elevation: Optional[Union[float, torch.FloatTensor]] = None, + azimuth: Optional[Union[float, torch.FloatTensor]] = None, + distance: Optional[Union[float, torch.FloatTensor]] = None, + height: Optional[int] = None, + width: Optional[int] = None, + num_inference_steps: int = 50, + guidance_scale: float = 3.0, + num_images_per_prompt: int = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + clip_image_embeddings: Optional[torch.FloatTensor] = None, + image_camera_embeddings: Optional[torch.FloatTensor] = None, + image_latents: Optional[torch.FloatTensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: int = 1, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + ): + r""" + Function invoked when calling the pipeline for generation. + + Args: + image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`): + The image or images to guide the image generation. If you provide a tensor, it needs to comply with the + configuration of + [this](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json) + `CLIPImageProcessor` + height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The width in pixels of the generated image. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + guidance_scale (`float`, *optional*, defaults to 7.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator`, *optional*): + One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) + to make generation deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor will ge generated by sampling using the supplied random `generator`. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + + Returns: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. + When returning a tuple, the first element is a list with the generated images, and the second element is a + list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" + (nsfw) content, according to the `safety_checker`. + """ + # 0. Default height and width to unet + height = height or self.unet.config.sample_size * self.vae_scale_factor + width = width or self.unet.config.sample_size * self.vae_scale_factor + + # 1. Check inputs. Raise error if not correct + # TODO: check input elevation, azimuth, and distance + # TODO: check image, clip_image_embeddings, image_latents + self.check_inputs(image, height, width, callback_steps) + + # 2. Define call parameters + if isinstance(image, PIL.Image.Image): + batch_size = 1 + elif isinstance(image, list): + batch_size = len(image) + elif isinstance(image, torch.Tensor): + batch_size = image.shape[0] + else: + assert image_latents is not None + assert ( + clip_image_embeddings is not None or image_camera_embeddings is not None + ) + batch_size = image_latents.shape[0] + + device = self._execution_device + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # 3. Encode input image + if isinstance(image, PIL.Image.Image) or isinstance(image, list): + pil_image = image + elif isinstance(image, torch.Tensor): + pil_image = [TF.to_pil_image(image[i]) for i in range(image.shape[0])] + else: + pil_image = None + image_embeddings = self._encode_image( + pil_image, + elevation, + azimuth, + distance, + device, + num_images_per_prompt, + do_classifier_free_guidance, + clip_image_embeddings, + image_camera_embeddings, + ) + + # 4. Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + # 5. Prepare latent variables + # num_channels_latents = self.unet.config.in_channels + num_channels_latents = 4 # FIXME: hard-coded + latents = self.prepare_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + height, + width, + image_embeddings.dtype, + device, + generator, + latents, + ) + + # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # 7. Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + latent_model_input = self._get_latent_model_input( + latents, + image, + num_images_per_prompt, + do_classifier_free_guidance, + image_latents, + ) + latent_model_input = self.scheduler.scale_model_input( + latent_model_input, t + ) + + # predict the noise residual + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=image_embeddings, + cross_attention_kwargs=cross_attention_kwargs, + ).sample + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step( + noise_pred, t, latents, **extra_step_kwargs + ).prev_sample + + # call the callback, if provided + if i == len(timesteps) - 1 or ( + (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0 + ): + progress_bar.update() + if callback is not None and i % callback_steps == 0: + callback(i, t, latents) + + if not output_type == "latent": + image = self.vae.decode( + latents / self.vae.config.scaling_factor, return_dict=False + )[0] + image, has_nsfw_concept = self.run_safety_checker( + image, device, image_embeddings.dtype + ) + else: + image = latents + has_nsfw_concept = None + + if has_nsfw_concept is None: + do_denormalize = [True] * image.shape[0] + else: + do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] + + image = self.image_processor.postprocess( + image, output_type=output_type, do_denormalize=do_denormalize + ) + + if not return_dict: + return (image, has_nsfw_concept) + + return StableDiffusionPipelineOutput( + images=image, nsfw_content_detected=has_nsfw_concept + ) diff --git a/gradio_app.py b/gradio_app.py new file mode 100644 index 0000000..0d921d9 --- /dev/null +++ b/gradio_app.py @@ -0,0 +1,544 @@ +import argparse +import glob +import os +import re +import signal +import subprocess +import tempfile +import time +from dataclasses import dataclass +from datetime import datetime +from typing import Optional + +import gradio as gr +import numpy as np +import psutil +import trimesh + +from threestudio.utils.config import load_config +from threestudio.utils.typing import * + + +def tail(f, window=20): + # Returns the last `window` lines of file `f`. + if window == 0: + return [] + + BUFSIZ = 1024 + f.seek(0, 2) + remaining_bytes = f.tell() + size = window + 1 + block = -1 + data = [] + + while size > 0 and remaining_bytes > 0: + if remaining_bytes - BUFSIZ > 0: + # Seek back one whole BUFSIZ + f.seek(block * BUFSIZ, 2) + # read BUFFER + bunch = f.read(BUFSIZ) + else: + # file too small, start from beginning + f.seek(0, 0) + # only read what was not read + bunch = f.read(remaining_bytes) + + bunch = bunch.decode("utf-8") + data.insert(0, bunch) + size -= bunch.count("\n") + remaining_bytes -= BUFSIZ + block -= 1 + + return "\n".join("".join(data).splitlines()[-window:]) + + +@dataclass +class ExperimentStatus: + pid: Optional[int] = None + progress: str = "" + log: str = "" + output_image: Optional[str] = None + output_video: Optional[str] = None + output_mesh: Optional[str] = None + + def tolist(self): + return [ + self.pid, + self.progress, + self.log, + self.output_image, + self.output_video, + self.output_mesh, + ] + + +EXP_ROOT_DIR = "outputs-gradio" +DEFAULT_PROMPT = "a delicious hamburger" +model_name_config = [ + ("SJC (Stable Diffusion)", "configs/gradio/sjc.yaml"), + ("DreamFusion (DeepFloyd-IF)", "configs/gradio/dreamfusion-if.yaml"), + ("DreamFusion (Stable Diffusion)", "configs/gradio/dreamfusion-sd.yaml"), + ("TextMesh (DeepFloyd-IF)", "configs/gradio/textmesh-if.yaml"), + ("Latent-NeRF (Stable Diffusion)", "configs/gradio/latentnerf.yaml"), + ("Fantasia3D (Stable Diffusion, Geometry Only)", "configs/gradio/fantasia3d.yaml"), +] +model_list = [m[0] for m in model_name_config] +model_config: Dict[str, Dict[str, Any]] = {} + +for model_name, config_path in model_name_config: + config = {"path": config_path} + with open(config_path) as f: + config["yaml"] = f.read() + config["obj"] = load_config( + config["yaml"], + # set name and tag to dummy values to avoid creating new directories + cli_args=[ + "name=dummy", + "tag=dummy", + "use_timestamp=false", + f"exp_root_dir={EXP_ROOT_DIR}", + "system.prompt_processor.prompt=placeholder", + ], + from_string=True, + ) + model_config[model_name] = config + + +def on_model_selector_change(model_name): + return [ + model_config[model_name]["yaml"], + model_config[model_name]["obj"].system.guidance.guidance_scale, + ] + + +def get_current_status(process, trial_dir, alive_path): + status = ExperimentStatus() + + status.pid = process.pid + + # write the current timestamp to the alive file + # the watcher will know the last active time of this process from this timestamp + if os.path.exists(os.path.dirname(alive_path)): + alive_fp = open(alive_path, "w") + alive_fp.seek(0) + alive_fp.write(str(time.time())) + alive_fp.flush() + + log_path = os.path.join(trial_dir, "logs") + progress_path = os.path.join(trial_dir, "progress") + save_path = os.path.join(trial_dir, "save") + + # read current progress from the progress file + # the progress file is created by GradioCallback + if os.path.exists(progress_path): + status.progress = open(progress_path).read() + else: + status.progress = "Setting up everything ..." + + # read the last 10 lines of the log file + if os.path.exists(log_path): + status.log = tail(open(log_path, "rb"), window=10) + else: + status.log = "" + + # get the validation image and testing video if they exist + if os.path.exists(save_path): + images = glob.glob(os.path.join(save_path, "*.png")) + steps = [ + int(re.match(r"it(\d+)-0\.png", os.path.basename(f)).group(1)) + for f in images + ] + images = sorted(list(zip(images, steps)), key=lambda x: x[1]) + if len(images) > 0: + status.output_image = images[-1][0] + + videos = glob.glob(os.path.join(save_path, "*.mp4")) + steps = [ + int(re.match(r"it(\d+)-test\.mp4", os.path.basename(f)).group(1)) + for f in videos + ] + videos = sorted(list(zip(videos, steps)), key=lambda x: x[1]) + if len(videos) > 0: + status.output_video = videos[-1][0] + + export_dirs = glob.glob(os.path.join(save_path, "*export")) + steps = [ + int(re.match(r"it(\d+)-export", os.path.basename(f)).group(1)) + for f in export_dirs + ] + export_dirs = sorted(list(zip(export_dirs, steps)), key=lambda x: x[1]) + if len(export_dirs) > 0: + obj = glob.glob(os.path.join(export_dirs[-1][0], "*.obj")) + if len(obj) > 0: + # FIXME + # seems the gr.Model3D cannot load our manually saved obj file + # here we load the obj and save it to a temporary file using trimesh + mesh_path = tempfile.NamedTemporaryFile(suffix=".obj", delete=False) + trimesh.load(obj[0]).export(mesh_path.name) + status.output_mesh = mesh_path.name + + return status + + +def run( + model_name: str, + config: str, + prompt: str, + guidance_scale: float, + seed: int, + max_steps: int, + save_ckpt: bool, + save_root: str, +): + # update status every 1 second + status_update_interval = 1 + + # save the config to a temporary file + config_file = tempfile.NamedTemporaryFile() + + with open(config_file.name, "w") as f: + f.write(config) + + # manually assign the output directory, name and tag so that we know the trial directory + name = os.path.basename(model_config[model_name]["path"]).split(".")[0] + tag = datetime.now().strftime("%Y%m%d-%H%M%S") + trial_dir = os.path.join(save_root, EXP_ROOT_DIR, name, tag) + alive_path = os.path.join(trial_dir, "alive") + + # spawn the training process + gpu = os.environ.get("CUDA_VISIBLE_DEVICES", "0") + process = subprocess.Popen( + f"python launch.py --config {config_file.name} --train --gpu {gpu} --gradio trainer.enable_progress_bar=false".split() + + [ + f'name="{name}"', + f'tag="{tag}"', + f"exp_root_dir={os.path.join(save_root, EXP_ROOT_DIR)}", + "use_timestamp=false", + f'system.prompt_processor.prompt="{prompt}"', + f"system.guidance.guidance_scale={guidance_scale}", + f"seed={seed}", + f"trainer.max_steps={max_steps}", + ] + + ( + ["checkpoint.every_n_train_steps=${trainer.max_steps}"] if save_ckpt else [] + ), + ) + + # spawn the watcher process + watch_process = subprocess.Popen( + "python gradio_app.py watch".split() + + ["--pid", f"{process.pid}", "--trial-dir", f"{trial_dir}"] + ) + + # update status (progress, log, image, video) every status_update_interval senconds + # button status: Run -> Stop + while process.poll() is None: + time.sleep(status_update_interval) + yield get_current_status(process, trial_dir, alive_path).tolist() + [ + gr.update(visible=False), + gr.update(value="Stop", variant="stop", visible=True), + ] + + # wait for the processes to finish + process.wait() + watch_process.wait() + + # update status one last time + # button status: Stop / Reset -> Run + status = get_current_status(process, trial_dir, alive_path) + status.progress = "Finished." + yield status.tolist() + [ + gr.update(value="Run", variant="primary", visible=True), + gr.update(visible=False), + ] + + +def stop_run(pid): + # kill the process + print(f"Trying to kill process {pid} ...") + try: + os.kill(pid, signal.SIGKILL) + except: + print(f"Exception when killing process {pid}.") + # button status: Stop -> Reset + return [ + # gr.update( + # value="Reset (refresh the page if in queue)", + # variant="secondary", + # visible=True, + # just ask the user to refresh the page + # ), + gr.update( + value="Please Refresh the Page", + variant="secondary", + visible=True, + interactive=False, + ), + gr.update(visible=False), + ] + + +def launch( + port, + listen=False, + hf_space=False, + self_deploy=False, + save_ckpt=False, + save_root=".", +): + self_deploy = self_deploy or "TS_SELF_DEPLOY" in os.environ + + css = """ + #config-accordion, #logs-accordion {color: black !important;} + .dark #config-accordion, .dark #logs-accordion {color: white !important;} + .stop {background: darkred !important;} + """ + + with gr.Blocks( + title="threestudio - Web Demo", + theme=gr.themes.Monochrome(), + css=css, + ) as demo: + with gr.Row(equal_height=True): + if hf_space: + header = """ + # threestudio Text-to-3D Web Demo + +
+ + Duplicate Space +
+ + ### Usage + - Select a model from the dropdown menu. If you duplicate this space and would like to use models based on DeepFloyd-IF, you need to [accept the license](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0) and set `HUGGING_FACE_HUB_TOKEN` in `Repository secrets` in your space setting. You may also set `TS_SELF_DEPLOY` to enable changing arbitrary configurations. + - Input a text prompt and hit the `Run` button to start. + - Video and mesh export (not supported for SJC and Latent-NeRF) are available after the training process is finished. + - **IMPORTANT NOTE: Keep this tab active when running the model.** + """ + else: + header = """ + # threestudio Text-to-3D Web Demo + + ### Usage + - Select a model from the dropdown menu. + - Input a text prompt and hit the `Run` button to start. + - Video and mesh export (not supported for SJC and Latent-NeRF) are available after the training process is finished. + - **IMPORTANT NOTE: Keep this tab active when running the model.** + """ + gr.Markdown(header) + + with gr.Row(equal_height=False): + pid = gr.State() + with gr.Column(scale=1): + # generation status + status = gr.Textbox( + value="Hit the Run button to start.", + label="Status", + lines=1, + max_lines=1, + ) + + # model selection dropdown + model_selector = gr.Dropdown( + value=model_list[0], + choices=model_list, + label="Select a model", + ) + + # prompt input + prompt_input = gr.Textbox(value=DEFAULT_PROMPT, label="Input prompt") + + # guidance scale slider + guidance_scale_input = gr.Slider( + minimum=0.0, + maximum=100.0, + value=model_config[model_selector.value][ + "obj" + ].system.guidance.guidance_scale, + step=0.5, + label="Guidance scale", + ) + + # seed slider + seed_input = gr.Slider( + minimum=0, maximum=2147483647, value=0, step=1, label="Seed" + ) + + max_steps_input = gr.Slider( + minimum=1, + maximum=20000 if self_deploy else 5000, + value=10000 if self_deploy else 5000, + step=1, + label="Number of training steps", + ) + + save_ckpt_checkbox = gr.Checkbox( + value=save_ckpt, + label="Save Checkpoints", + visible=False, + interactive=False, + ) + + save_root_state = gr.State(value=save_root) + + # full config viewer + with gr.Accordion( + "See full configurations", open=False, elem_id="config-accordion" + ): + config_editor = gr.Code( + value=model_config[model_selector.value]["yaml"], + language="yaml", + lines=10, + interactive=self_deploy, # disable editing if in HF space + ) + + # load config on model selection change + model_selector.change( + fn=on_model_selector_change, + inputs=model_selector, + outputs=[config_editor, guidance_scale_input], + queue=False, + ) + + run_btn = gr.Button(value="Run", variant="primary") + stop_btn = gr.Button(value="Stop", variant="stop", visible=False) + + with gr.Column(scale=1): + with gr.Accordion( + "See terminal logs", open=False, elem_id="logs-accordion" + ): + # logs + logs = gr.Textbox(label="Logs", lines=10) + + # validation image display + output_image = gr.Image(value=None, label="Image") + + # testing video display + output_video = gr.Video(value=None, label="Video") + + # export mesh display + output_mesh = gr.Model3D(value=None, label="3D Mesh") + + run_event = run_btn.click( + fn=run, + inputs=[ + model_selector, + config_editor, + prompt_input, + guidance_scale_input, + seed_input, + max_steps_input, + save_ckpt_checkbox, + save_root_state, + ], + outputs=[ + pid, + status, + logs, + output_image, + output_video, + output_mesh, + run_btn, + stop_btn, + ], + concurrency_limit=1, + ) + stop_btn.click( + fn=stop_run, + inputs=[pid], + outputs=[run_btn, stop_btn], + cancels=[run_event], + queue=False, + ) + + launch_args = {"server_port": port} + if listen: + launch_args["server_name"] = "0.0.0.0" + demo.queue().launch(**launch_args) + + +def watch( + pid: int, + trial_dir: str, + alive_timeout: int, + wait_timeout: int, + check_interval: int, +) -> None: + print(f"Spawn watcher for process {pid}") + + def timeout_handler(signum, frame): + exit(1) + + alive_path = os.path.join(trial_dir, "alive") + signal.signal(signal.SIGALRM, timeout_handler) + signal.alarm(wait_timeout) + + def loop_find_progress_file(): + while True: + if not os.path.exists(alive_path): + time.sleep(check_interval) + else: + signal.alarm(0) + return + + def loop_check_alive(): + while True: + if not psutil.pid_exists(pid): + print(f"Process {pid} not exists, watcher exits.") + cleanup_and_exit() + try: + alive_timestamp = float(open(alive_path).read()) + except: + continue + if time.time() - alive_timestamp > alive_timeout: + print(f"Alive timeout for process {pid}, killed.") + try: + os.kill(pid, signal.SIGKILL) + except: + print(f"Exception when killing process {pid}.") + cleanup_and_exit() + time.sleep(check_interval) + + def cleanup_and_exit(): + exit(0) + + # loop until alive file is found, or alive_timeout is reached + loop_find_progress_file() + # kill the process if it is not accessed for alive_timeout seconds + loop_check_alive() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("operation", type=str, choices=["launch", "watch"]) + args, extra = parser.parse_known_args() + if args.operation == "launch": + parser.add_argument("--listen", action="store_true") + parser.add_argument("--hf-space", action="store_true") + parser.add_argument("--self-deploy", action="store_true") + parser.add_argument("--save-ckpt", action="store_true") # unused + parser.add_argument("--save-root", type=str, default=".") + parser.add_argument("--port", type=int, default=7860) + args = parser.parse_args() + launch( + args.port, + listen=args.listen, + hf_space=args.hf_space, + self_deploy=args.self_deploy, + save_ckpt=args.save_ckpt, + save_root=args.save_root, + ) + if args.operation == "watch": + parser.add_argument("--pid", type=int) + parser.add_argument("--trial-dir", type=str) + parser.add_argument("--alive-timeout", type=int, default=10) + parser.add_argument("--wait-timeout", type=int, default=10) + parser.add_argument("--check-interval", type=int, default=1) + args = parser.parse_args() + watch( + args.pid, + args.trial_dir, + alive_timeout=args.alive_timeout, + wait_timeout=args.wait_timeout, + check_interval=args.check_interval, + ) diff --git a/launch.py b/launch.py new file mode 100644 index 0000000..bca4ae1 --- /dev/null +++ b/launch.py @@ -0,0 +1,301 @@ +import argparse +import contextlib +import importlib +import logging +import os +import sys +import time +import traceback + + +class ColoredFilter(logging.Filter): + """ + A logging filter to add color to certain log levels. + """ + + RESET = "\033[0m" + RED = "\033[31m" + GREEN = "\033[32m" + YELLOW = "\033[33m" + BLUE = "\033[34m" + MAGENTA = "\033[35m" + CYAN = "\033[36m" + + COLORS = { + "WARNING": YELLOW, + "INFO": GREEN, + "DEBUG": BLUE, + "CRITICAL": MAGENTA, + "ERROR": RED, + } + + RESET = "\x1b[0m" + + def __init__(self): + super().__init__() + + def filter(self, record): + if record.levelname in self.COLORS: + color_start = self.COLORS[record.levelname] + record.levelname = f"{color_start}[{record.levelname}]" + record.msg = f"{record.msg}{self.RESET}" + return True + + +def load_custom_module(module_path): + module_name = os.path.basename(module_path) + if os.path.isfile(module_path): + sp = os.path.splitext(module_path) + module_name = sp[0] + try: + if os.path.isfile(module_path): + module_spec = importlib.util.spec_from_file_location( + module_name, module_path + ) + else: + module_spec = importlib.util.spec_from_file_location( + module_name, os.path.join(module_path, "__init__.py") + ) + + module = importlib.util.module_from_spec(module_spec) + sys.modules[module_name] = module + module_spec.loader.exec_module(module) + return True + except Exception as e: + print(traceback.format_exc()) + print(f"Cannot import {module_path} module for custom nodes:", e) + return False + + +def load_custom_modules(): + node_paths = ["custom"] + node_import_times = [] + for custom_node_path in node_paths: + possible_modules = os.listdir(custom_node_path) + if "__pycache__" in possible_modules: + possible_modules.remove("__pycache__") + + for possible_module in possible_modules: + module_path = os.path.join(custom_node_path, possible_module) + if ( + os.path.isfile(module_path) + and os.path.splitext(module_path)[1] != ".py" + ): + continue + if module_path.endswith("_disabled"): + continue + time_before = time.perf_counter() + success = load_custom_module(module_path) + node_import_times.append( + (time.perf_counter() - time_before, module_path, success) + ) + + if len(node_import_times) > 0: + print("\nImport times for custom modules:") + for n in sorted(node_import_times): + if n[2]: + import_message = "" + else: + import_message = " (IMPORT FAILED)" + print("{:6.1f} seconds{}:".format(n[0], import_message), n[1]) + print() + + +def main(args, extras) -> None: + # set CUDA_VISIBLE_DEVICES if needed, then import pytorch-lightning + os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" + env_gpus_str = os.environ.get("CUDA_VISIBLE_DEVICES", None) + env_gpus = list(env_gpus_str.split(",")) if env_gpus_str else [] + selected_gpus = [0] + + # Always rely on CUDA_VISIBLE_DEVICES if specific GPU ID(s) are specified. + # As far as Pytorch Lightning is concerned, we always use all available GPUs + # (possibly filtered by CUDA_VISIBLE_DEVICES). + devices = -1 + if len(env_gpus) > 0: + # CUDA_VISIBLE_DEVICES was set already, e.g. within SLURM srun or higher-level script. + n_gpus = len(env_gpus) + else: + selected_gpus = list(args.gpu.split(",")) + n_gpus = len(selected_gpus) + os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu + + import pytorch_lightning as pl + import torch + from pytorch_lightning import Trainer + from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint + from pytorch_lightning.loggers import CSVLogger, TensorBoardLogger + from pytorch_lightning.utilities.rank_zero import rank_zero_only + + if args.typecheck: + from jaxtyping import install_import_hook + + install_import_hook("threestudio", "typeguard.typechecked") + + import threestudio + from threestudio.systems.base import BaseSystem + from threestudio.utils.callbacks import ( + CodeSnapshotCallback, + ConfigSnapshotCallback, + CustomProgressBar, + ProgressCallback, + ) + from threestudio.utils.config import ExperimentConfig, load_config + from threestudio.utils.misc import get_rank + from threestudio.utils.typing import Optional + + logger = logging.getLogger("pytorch_lightning") + if args.verbose: + logger.setLevel(logging.DEBUG) + + for handler in logger.handlers: + if handler.stream == sys.stderr: # type: ignore + if not args.gradio: + handler.setFormatter(logging.Formatter("%(levelname)s %(message)s")) + handler.addFilter(ColoredFilter()) + else: + handler.setFormatter(logging.Formatter("[%(levelname)s] %(message)s")) + + load_custom_modules() + + # parse YAML config to OmegaConf + cfg: ExperimentConfig + cfg = load_config(args.config, cli_args=extras, n_gpus=n_gpus) + + # set a different seed for each device + pl.seed_everything(cfg.seed + get_rank(), workers=True) + + dm = threestudio.find(cfg.data_type)(cfg.data) + system: BaseSystem = threestudio.find(cfg.system_type)( + cfg.system, resumed=cfg.resume is not None + ) + system.set_save_dir(os.path.join(cfg.trial_dir, "save")) + + if args.gradio: + fh = logging.FileHandler(os.path.join(cfg.trial_dir, "logs")) + fh.setLevel(logging.INFO) + if args.verbose: + fh.setLevel(logging.DEBUG) + fh.setFormatter(logging.Formatter("[%(levelname)s] %(message)s")) + logger.addHandler(fh) + + callbacks = [] + if args.train: + callbacks += [ + ModelCheckpoint( + dirpath=os.path.join(cfg.trial_dir, "ckpts"), **cfg.checkpoint + ), + LearningRateMonitor(logging_interval="step"), + CodeSnapshotCallback( + os.path.join(cfg.trial_dir, "code"), use_version=False + ), + ConfigSnapshotCallback( + args.config, + cfg, + os.path.join(cfg.trial_dir, "configs"), + use_version=False, + ), + ] + if args.gradio: + callbacks += [ + ProgressCallback(save_path=os.path.join(cfg.trial_dir, "progress")) + ] + else: + callbacks += [CustomProgressBar(refresh_rate=1)] + + def write_to_text(file, lines): + with open(file, "w") as f: + for line in lines: + f.write(line + "\n") + + loggers = [] + if args.train: + # make tensorboard logging dir to suppress warning + rank_zero_only( + lambda: os.makedirs(os.path.join(cfg.trial_dir, "tb_logs"), exist_ok=True) + )() + loggers += [ + TensorBoardLogger(cfg.trial_dir, name="tb_logs"), + CSVLogger(cfg.trial_dir, name="csv_logs"), + ] + system.get_loggers() + rank_zero_only( + lambda: write_to_text( + os.path.join(cfg.trial_dir, "cmd.txt"), + ["python " + " ".join(sys.argv), str(args)], + ) + )() + + trainer = Trainer( + callbacks=callbacks, + logger=loggers, + inference_mode=False, + accelerator="gpu", + devices=devices, + **cfg.trainer, + ) + + def set_system_status(system: BaseSystem, ckpt_path: Optional[str]): + if ckpt_path is None: + return + ckpt = torch.load(ckpt_path, map_location="cpu") + system.set_resume_status(ckpt["epoch"], ckpt["global_step"]) + + if args.train: + trainer.fit(system, datamodule=dm, ckpt_path=cfg.resume) + trainer.test(system, datamodule=dm) + if args.gradio: + # also export assets if in gradio mode + trainer.predict(system, datamodule=dm) + elif args.validate: + # manually set epoch and global_step as they cannot be automatically resumed + set_system_status(system, cfg.resume) + trainer.validate(system, datamodule=dm, ckpt_path=cfg.resume) + elif args.test: + # manually set epoch and global_step as they cannot be automatically resumed + set_system_status(system, cfg.resume) + trainer.test(system, datamodule=dm, ckpt_path=cfg.resume) + elif args.export: + set_system_status(system, cfg.resume) + trainer.predict(system, datamodule=dm, ckpt_path=cfg.resume) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--config", required=True, help="path to config file") + parser.add_argument( + "--gpu", + default="0", + help="GPU(s) to be used. 0 means use the 1st available GPU. " + "1,2 means use the 2nd and 3rd available GPU. " + "If CUDA_VISIBLE_DEVICES is set before calling `launch.py`, " + "this argument is ignored and all available GPUs are always used.", + ) + + group = parser.add_mutually_exclusive_group(required=True) + group.add_argument("--train", action="store_true") + group.add_argument("--validate", action="store_true") + group.add_argument("--test", action="store_true") + group.add_argument("--export", action="store_true") + + parser.add_argument( + "--gradio", action="store_true", help="if true, run in gradio mode" + ) + + parser.add_argument( + "--verbose", action="store_true", help="if true, set logging level to DEBUG" + ) + + parser.add_argument( + "--typecheck", + action="store_true", + help="whether to enable dynamic type checking", + ) + + args, extras = parser.parse_known_args() + + if args.gradio: + # FIXME: no effect, stdout is not captured + with contextlib.redirect_stdout(sys.stderr): + main(args, extras) + else: + main(args, extras) diff --git a/load/images/anya_front.png 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mud_road_puresky.hdr HDR probe is from https://polyhaven.com/a/mud_road_puresky +CC0 License. diff --git a/load/lights/bsdf_256_256.bin b/load/lights/bsdf_256_256.bin new file mode 100644 index 0000000..feb212d Binary files /dev/null and b/load/lights/bsdf_256_256.bin differ diff --git a/load/lights/mud_road_puresky_1k.hdr b/load/lights/mud_road_puresky_1k.hdr new file mode 100644 index 0000000..3574b7f Binary files /dev/null and b/load/lights/mud_road_puresky_1k.hdr differ diff --git a/load/make_prompt_library.py b/load/make_prompt_library.py new file mode 100644 index 0000000..c142004 --- /dev/null +++ b/load/make_prompt_library.py @@ -0,0 +1,434 @@ +import json + +dreamfusion_gallery_video_names = [ + "a_20-sided_die_made_out_of_glass.mp4", + "a_bald_eagle_carved_out_of_wood.mp4", + "a_banana_peeling_itself.mp4", + "a_beagle_in_a_detective's_outfit.mp4", + "a_beautiful_dress_made_out_of_fruit,_on_a_mannequin._Studio_lighting,_high_quality,_high_resolution.mp4", + "a_beautiful_dress_made_out_of_garbage_bags,_on_a_mannequin._Studio_lighting,_high_quality,_high_resolution.mp4", + "a_beautiful_rainbow_fish.mp4", + "a_bichon_frise_wearing_academic_regalia.mp4", + "a_blue_motorcycle.mp4", + "a_blue_poison-dart_frog_sitting_on_a_water_lily.mp4", + "a_brightly_colored_mushroom_growing_on_a_log.mp4", + "a_bumblebee_sitting_on_a_pink_flower.mp4", + "a_bunch_of_colorful_marbles_spilling_out_of_a_red_velvet_bag.mp4", + "a_capybara_wearing_a_top_hat,_low_poly.mp4", + "a_cat_with_a_mullet.mp4", + "a_ceramic_lion.mp4", + "a_ceramic_upside_down_yellow_octopus_holding_a_blue_green_ceramic_cup.mp4", + "a_chihuahua_wearing_a_tutu.mp4", + "a_chimpanzee_holding_a_peeled_banana.mp4", + "a_chimpanzee_looking_through_a_telescope.mp4", + "a_chimpanzee_stirring_a_bubbling_purple_potion_in_a_cauldron.mp4", + "a_chimpanzee_with_a_big_grin.mp4", + "a_completely_destroyed_car.mp4", + "a_confused_beagle_sitting_at_a_desk_working_on_homework.mp4", + "a_corgi_taking_a_selfie.mp4", + "a_crab,_low_poly.mp4", + "a_crocodile_playing_a_drum_set.mp4", + "a_cute_steampunk_elephant.mp4", + "a_dachsund_dressed_up_in_a_hotdog_costume.mp4", + "a_delicious_hamburger.mp4", + "a_dragon-cat_hybrid.mp4", + "a_DSLR_photo_of_a_baby_dragon_drinking_boba.mp4", + "a_DSLR_photo_of_a_baby_dragon_hatching_out_of_a_stone_egg.mp4", + "a_DSLR_photo_of_a_baby_grand_piano_viewed_from_far_away.mp4", + "a_DSLR_photo_of_a_bagel_filled_with_cream_cheese_and_lox.mp4", + "a_DSLR_photo_of_a_bald_eagle.mp4", + "a_DSLR_photo_of_a_barbecue_grill_cooking_sausages_and_burger_patties.mp4", + "a_DSLR_photo_of_a_basil_plant.mp4", + "a_DSLR_photo_of_a_bear_dancing_ballet.mp4", + "a_DSLR_photo_of_a_bear_dressed_as_a_lumberjack.mp4", + "a_DSLR_photo_of_a_bear_dressed_in_medieval_armor.mp4", + "a_DSLR_photo_of_a_beautiful_violin_sitting_flat_on_a_table.mp4", + "a_DSLR_photo_of_a_blue_jay_standing_on_a_large_basket_of_rainbow_macarons.mp4", + "a_DSLR_photo_of_a_bulldozer_clearing_away_a_pile_of_snow.mp4", + "a_DSLR_photo_of_a_bulldozer.mp4", + "a_DSLR_photo_of_a_cake_covered_in_colorful_frosting_with_a_slice_being_taken_out,_high_resolution.mp4", + "a_DSLR_photo_of_a_candelabra_with_many_candles_on_a_red_velvet_tablecloth.mp4", + "a_DSLR_photo_of_a_car_made_out_of_cheese.mp4", + "a_DSLR_photo_of_A_car_made_out_of_sushi.mp4", + "a_DSLR_photo_of_a_car_made_out_pizza.mp4", + "a_DSLR_photo_of_a_cat_lying_on_its_side_batting_at_a_ball_of_yarn.mp4", + "a_DSLR_photo_of_a_cat_magician_making_a_white_dove_appear.mp4", + "a_DSLR_photo_of_a_cat_wearing_a_bee_costume.mp4", + "a_DSLR_photo_of_a_cat_wearing_a_lion_costume.mp4", + "a_DSLR_photo_of_a_cauldron_full_of_gold_coins.mp4", + "a_DSLR_photo_of_a_chimpanzee_dressed_like_Henry_VIII_king_of_England.mp4", + "a_DSLR_photo_of_a_chimpanzee_dressed_like_Napoleon_Bonaparte.mp4", + "a_DSLR_photo_of_a_chow_chow_puppy.mp4", + "a_DSLR_photo_of_a_Christmas_tree_with_donuts_as_decorations.mp4", + "a_DSLR_photo_of_a_chrome-plated_duck_with_a_golden_beak_arguing_with_an_angry_turtle_in_a_forest.mp4", + "a_DSLR_photo_of_a_classic_Packard_car.mp4", + "a_DSLR_photo_of_a_cocker_spaniel_wearing_a_crown.mp4", + "a_DSLR_photo_of_a_corgi_lying_on_its_back_with_its_tongue_lolling_out.mp4", + "a_DSLR_photo_of_a_corgi_puppy.mp4", + "a_DSLR_photo_of_a_corgi_sneezing.mp4", + "a_DSLR_photo_of_a_corgi_standing_up_drinking_boba.mp4", + "a_DSLR_photo_of_a_corgi_taking_a_selfie.mp4", + "a_DSLR_photo_of_a_corgi_wearing_a_beret_and_holding_a_baguette,_standing_up_on_two_hind_legs.mp4", + "a_DSLR_photo_of_a_covered_wagon.mp4", + "a_DSLR_photo_of_a_cracked_egg_with_the_yolk_spilling_out_on_a_wooden_table.mp4", + "a_DSLR_photo_of_a_cup_full_of_pens_and_pencils.mp4", + "a_DSLR_photo_of_a_dalmation_wearing_a_fireman's_hat.mp4", + "a_DSLR_photo_of_a_delicious_chocolate_brownie_dessert_with_ice_cream_on_the_side.mp4", + "a_DSLR_photo_of_a_delicious_croissant.mp4", + "a_DSLR_photo_of_A_DMC_Delorean_car.mp4", + "a_DSLR_photo_of_a_dog_made_out_of_salad.mp4", + "a_DSLR_photo_of_a_drum_set_made_of_cheese.mp4", + "a_DSLR_photo_of_a_drying_rack_covered_in_clothes.mp4", + "a_DSLR_photo_of_aerial_view_of_a_ruined_castle.mp4", + "a_DSLR_photo_of_a_football_helmet.mp4", + "a_DSLR_photo_of_a_fox_holding_a_videogame_controller.mp4", + "a_DSLR_photo_of_a_fox_taking_a_photograph_using_a_DSLR.mp4", + "a_DSLR_photo_of_a_frazer_nash_super_sport_car.mp4", + "a_DSLR_photo_of_a_frog_wearing_a_sweater.mp4", + "a_DSLR_photo_of_a_ghost_eating_a_hamburger.mp4", + "a_DSLR_photo_of_a_giant_worm_emerging_from_the_sand_in_the_middle_of_the_desert.mp4", + "a_DSLR_photo_of_a_goose_made_out_of_gold.mp4", + "a_DSLR_photo_of_a_green_monster_truck.mp4", + "a_DSLR_photo_of_a_group_of_dogs_eating_pizza.mp4", + "a_DSLR_photo_of_a_group_of_dogs_playing_poker.mp4", + "a_DSLR_photo_of_a_gummy_bear_playing_the_saxophone.mp4", + "a_DSLR_photo_of_a_hippo_wearing_a_sweater.mp4", + "a_DSLR_photo_of_a_humanoid_robot_holding_a_human_brain.mp4", + "a_DSLR_photo_of_a_humanoid_robot_playing_solitaire.mp4", + "a_DSLR_photo_of_a_humanoid_robot_playing_the_cello.mp4", + "a_DSLR_photo_of_a_humanoid_robot_using_a_laptop.mp4", + "a_DSLR_photo_of_a_humanoid_robot_using_a_rolling_pin_to_roll_out_dough.mp4", + "a_DSLR_photo_of_a_human_skull.mp4", + "a_DSLR_photo_of_a_kitten_standing_on_top_of_a_giant_tortoise.mp4", + "a_DSLR_photo_of_a_knight_chopping_wood.mp4", + "a_DSLR_photo_of_a_knight_holding_a_lance_and_sitting_on_an_armored_horse.mp4", + "a_DSLR_photo_of_a_koala_wearing_a_party_hat_and_blowing_out_birthday_candles_on_a_cake.mp4", + "a_DSLR_photo_of_a_lemur_taking_notes_in_a_journal.mp4", + "a_DSLR_photo_of_a_lion_reading_the_newspaper.mp4", + "a_DSLR_photo_of_a_mandarin_duck_swimming_in_a_pond.mp4", + "a_DSLR_photo_of_a_model_of_the_eiffel_tower_made_out_of_toothpicks.mp4", + "a_DSLR_photo_of_a_mouse_playing_the_tuba.mp4", + "a_DSLR_photo_of_a_mug_of_hot_chocolate_with_whipped_cream_and_marshmallows.mp4", + "a_DSLR_photo_of_an_adorable_piglet_in_a_field.mp4", + "a_DSLR_photo_of_an_airplane_taking_off_from_the_runway.mp4", + "a_DSLR_photo_of_an_astronaut_standing_on_the_surface_of_mars.mp4", + "a_DSLR_photo_of_an_eggshell_broken_in_two_with_an_adorable_chick_standing_next_to_it.mp4", + "a_DSLR_photo_of_an_elephant_skull.mp4", + "a_DSLR_photo_of_an_exercise_bike_in_a_well_lit_room.mp4", + "a_DSLR_photo_of_an_extravagant_mansion,_aerial_view.mp4", + "a_DSLR_photo_of_an_ice_cream_sundae.mp4", + "a_DSLR_photo_of_an_iguana_holding_a_balloon.mp4", + "a_DSLR_photo_of_an_intricate_and_complex_dish_from_a_michelin_star_restaurant.mp4", + "a_DSLR_photo_of_An_iridescent_steampunk_patterned_millipede_with_bison_horns.mp4", + "a_DSLR_photo_of_an_octopus_playing_the_piano.mp4", + "a_DSLR_photo_of_an_old_car_overgrown_by_vines_and_weeds.mp4", + "a_DSLR_photo_of_an_old_vintage_car.mp4", + "a_DSLR_photo_of_an_orangutan_making_a_clay_bowl_on_a_throwing_wheel.mp4", + "a_DSLR_photo_of_an_orc_forging_a_hammer_on_an_anvil.mp4", + "a_DSLR_photo_of_an_origami_motorcycle.mp4", + "a_DSLR_photo_of_an_ornate_silver_gravy_boat_sitting_on_a_patterned_tablecloth.mp4", + "a_DSLR_photo_of_an_overstuffed_pastrami_sandwich.mp4", + "a_DSLR_photo_of_an_unstable_rock_cairn_in_the_middle_of_a_stream.mp4", + "a_DSLR_photo_of_a_pair_of_headphones_sitting_on_a_desk.mp4", + "a_DSLR_photo_of_a_pair_of_tan_cowboy_boots,_studio_lighting,_product_photography.mp4", + "a_DSLR_photo_of_a_peacock_on_a_surfboard.mp4", + "a_DSLR_photo_of_a_pigeon_reading_a_book.mp4", + "a_DSLR_photo_of_a_piglet_sitting_in_a_teacup.mp4", + "a_DSLR_photo_of_a_pig_playing_a_drum_set.mp4", + "a_DSLR_photo_of_a_pile_of_dice_on_a_green_tabletop_next_to_some_playing_cards.mp4", + "a_DSLR_photo_of_a_pirate_collie_dog,_high_resolution.mp4", + "a_DSLR_photo_of_a_plate_of_fried_chicken_and_waffles_with_maple_syrup_on_them.mp4", + "a_DSLR_photo_of_a_plate_piled_high_with_chocolate_chip_cookies.mp4", + "a_DSLR_photo_of_a_plush_t-rex_dinosaur_toy,_studio_lighting,_high_resolution.mp4", + "a_DSLR_photo_of_a_plush_triceratops_toy,_studio_lighting,_high_resolution.mp4", + "a_DSLR_photo_of_a_pomeranian_dog.mp4", + "a_DSLR_photo_of_a_porcelain_dragon.mp4", + "a_DSLR_photo_of_a_praying_mantis_wearing_roller_skates.mp4", + "a_DSLR_photo_of_a_puffin_standing_on_a_rock.mp4", + "a_DSLR_photo_of_a_pug_made_out_of_metal.mp4", + "a_DSLR_photo_of_a_pug_wearing_a_bee_costume.mp4", + "a_DSLR_photo_of_a_quill_and_ink_sitting_on_a_desk.mp4", + "a_DSLR_photo_of_a_raccoon_stealing_a_pie.mp4", + "a_DSLR_photo_of_a_red_cardinal_bird_singing.mp4", + "a_DSLR_photo_of_a_red_convertible_car_with_the_top_down.mp4", + "a_DSLR_photo_of_a_red-eyed_tree_frog.mp4", + "a_DSLR_photo_of_a_red_pickup_truck_driving_across_a_stream.mp4", + "a_DSLR_photo_of_a_red_wheelbarrow_with_a_shovel_in_it.mp4", + "a_DSLR_photo_of_a_roast_turkey_on_a_platter.mp4", + "a_DSLR_photo_of_a_robot_and_dinosaur_playing_chess,_high_resolution.mp4", + "a_DSLR_photo_of_a_robot_arm_picking_up_a_colorful_block_from_a_table.mp4", + "a_DSLR_photo_of_a_robot_cat_knocking_over_a_chess_piece_on_a_board.mp4", + "a_DSLR_photo_of_a_robot_dinosaur.mp4", + "a_DSLR_photo_of_a_robot_made_out_of_vegetables.mp4", + "a_DSLR_photo_of_a_robot_stegosaurus.mp4", + "a_DSLR_photo_of_a_robot_tiger.mp4", + "a_DSLR_photo_of_a_rolling_pin_on_top_of_bread_dough.mp4", + "a_DSLR_photo_of_a_sheepdog_running.mp4", + "a_DSLR_photo_of_a_shiba_inu_playing_golf_wearing_tartan_golf_clothes_and_hat.mp4", + "a_DSLR_photo_of_a_shiny_silver_robot_cat.mp4", + "a_DSLR_photo_of_a_silverback_gorilla_holding_a_golden_trophy.mp4", + "a_DSLR_photo_of_a_silver_humanoid_robot_flipping_a_coin.mp4", + "a_DSLR_photo_of_a_small_cherry_tomato_plant_in_a_pot_with_a_few_red_tomatoes_growing_on_it.mp4", + "a_DSLR_photo_of_a_small_saguaro_cactus_planted_in_a_clay_pot.mp4", + "a_DSLR_photo_of_a_Space_Shuttle.mp4", + "a_DSLR_photo_of_a_squirrel_dressed_like_a_clown.mp4", + "a_DSLR_photo_of_a_squirrel_flying_a_biplane.mp4", + "a_DSLR_photo_of_a_squirrel_giving_a_lecture_writing_on_a_chalkboard.mp4", + "a_DSLR_photo_of_a_squirrel_holding_a_bowling_ball.mp4", + "a_DSLR_photo_of_a_squirrel-lizard_hybrid.mp4", + "a_DSLR_photo_of_a_squirrel_made_out_of_fruit.mp4", + "a_DSLR_photo_of_a_squirrel-octopus_hybrid.mp4", + "a_DSLR_photo_of_a_stack_of_pancakes_covered_in_maple_syrup.mp4", + "a_DSLR_photo_of_a_steam_engine_train,_high_resolution.mp4", + "a_DSLR_photo_of_a_steaming_basket_full_of_dumplings.mp4", + "a_DSLR_photo_of_a_steaming_hot_plate_piled_high_with_spaghetti_and_meatballs.mp4", + "a_DSLR_photo_of_a_steampunk_space_ship_designed_in_the_18th_century.mp4", + "a_DSLR_photo_of_a_straw_basket_with_a_cobra_coming_out_of_it.mp4", + "a_DSLR_photo_of_a_swan_and_its_cygnets_swimming_in_a_pond.mp4", + "a_DSLR_photo_of_a_tarantula,_highly_detailed.mp4", + "a_DSLR_photo_of_a_teal_moped.mp4", + "a_DSLR_photo_of_a_teapot_shaped_like_an_elephant_head_where_its_snout_acts_as_the_spout.mp4", + "a_DSLR_photo_of_a_teddy_bear_taking_a_selfie.mp4", + "a_DSLR_photo_of_a_terracotta_bunny.mp4", + "a_DSLR_photo_of_a_tiger_dressed_as_a_doctor.mp4", + "a_DSLR_photo_of_a_tiger_made_out_of_yarn.mp4", + "a_DSLR_photo_of_a_toilet_made_out_of_gold.mp4", + "a_DSLR_photo_of_a_toy_robot.mp4", + "a_DSLR_photo_of_a_train_engine_made_out_of_clay.mp4", + "a_DSLR_photo_of_a_tray_of_Sushi_containing_pugs.mp4", + "a_DSLR_photo_of_a_tree_stump_with_an_axe_buried_in_it.mp4", + "a_DSLR_photo_of_a_turtle_standing_on_its_hind_legs,_wearing_a_top_hat_and_holding_a_cane.mp4", + "a_DSLR_photo_of_a_very_beautiful_small_organic_sculpture_made_of_fine_clockwork_and_gears_with_tiny_ruby_bearings,_very_intricate,_caved,_curved._Studio_lighting,_High_resolution,_white_background.mp4", + "a_DSLR_photo_of_A_very_beautiful_tiny_human_heart_organic_sculpture_made_of_copper_wire_and_threaded_pipes,_very_intricate,_curved,_Studio_lighting,_high_resolution.mp4", + "a_DSLR_photo_of_a_very_cool_and_trendy_pair_of_sneakers,_studio_lighting.mp4", + "a_DSLR_photo_of_a_vintage_record_player.mp4", + "a_DSLR_photo_of_a_wine_bottle_and_full_wine_glass_on_a_chessboard.mp4", + "a_DSLR_photo_of_a_wooden_desk_and_chair_from_an_elementary_school.mp4", + "a_DSLR_photo_of_a_yorkie_dog_eating_a_donut.mp4", + "a_DSLR_photo_of_a_yorkie_dog_wearing_extremely_cool_sneakers.mp4", + "a_DSLR_photo_of_baby_elephant_jumping_on_a_trampoline.mp4", + "a_DSLR_photo_of_cat_wearing_virtual_reality_headset_in_renaissance_oil_painting_high_detail_caravaggio.mp4", + "a_DSLR_photo_of_edible_typewriter_made_out_of_vegetables.mp4", + "a_DSLR_photo_of_Mont_Saint-Michel,_France,_aerial_view.mp4", + "a_DSLR_photo_of_Mount_Fuji,_aerial_view.mp4", + "a_DSLR_photo_of_Neuschwanstein_Castle,_aerial_view.mp4", + "A_DSLR_photo_of___pyramid_shaped_burrito_with_a_slice_cut_out_of_it.mp4", + "a_DSLR_photo_of_the_Imperial_State_Crown_of_England.mp4", + "a_DSLR_photo_of_the_leaning_tower_of_Pisa,_aerial_view.mp4", + "a_DSLR_photo_of_the_Statue_of_Liberty,_aerial_view.mp4", + "a_DSLR_photo_of_Two_locomotives_playing_tug_of_war.mp4", + "a_DSLR_photo_of_two_macaw_parrots_sharing_a_milkshake_with_two_straws.mp4", + "a_DSLR_photo_of_Westminster_Abbey,_aerial_view.mp4", + "a_ficus_planted_in_a_pot.mp4", + "a_flower_made_out_of_metal.mp4", + "a_fluffy_cat_lying_on_its_back_in_a_patch_of_sunlight.mp4", + "a_fox_and_a_hare_tangoing_together.mp4", + "a_fox_holding_a_videogame_controller.mp4", + "a_fox_playing_the_cello.mp4", + "a_frazer_nash_super_sport_car.mp4", + "a_freshly_baked_loaf_of_sourdough_bread_on_a_cutting_board.mp4", + "a_goat_drinking_beer.mp4", + "a_golden_goblet,_low_poly.mp4", + "a_green_dragon_breathing_fire.mp4", + "a_green_tractor_farming_corn_fields.mp4", + "a_highland_cow.mp4", + "a_hotdog_in_a_tutu_skirt.mp4", + "a_humanoid_robot_laying_on_the_couch_while_on_a_laptop.mp4", + "a_humanoid_robot_playing_the_violin.mp4", + "a_humanoid_robot_sitting_looking_at_a_Go_board_with_some_pieces_on_it.mp4", + "a_human_skeleton_drinking_a_glass_of_red_wine.mp4", + "a_human_skull_with_a_vine_growing_through_one_of_the_eye_sockets.mp4", + "a_kitten_looking_at_a_goldfish_in_a_bowl.mp4", + "a_lemur_drinking_boba.mp4", + "a_lemur_taking_notes_in_a_journal.mp4", + "a_lionfish.mp4", + "a_llama_wearing_a_suit.mp4", + "a_marble_bust_of_a_mouse.mp4", + "a_metal_sculpture_of_a_lion's_head,_highly_detailed.mp4", + "a_mojito_in_a_beach_chair.mp4", + "a_monkey-rabbit_hybrid.mp4", + "an_airplane_made_out_of_wood.mp4", + "an_amigurumi_bulldozer.mp4", + "An_anthropomorphic_tomato_eating_another_tomato.mp4", + "an_astronaut_playing_the_violin.mp4", + "an_astronaut_riding_a_kangaroo.mp4", + "an_English_castle,_aerial_view.mp4", + "an_erupting_volcano,_aerial_view.mp4", + "a_nest_with_a_few_white_eggs_and_one_golden_egg.mp4", + "an_exercise_bike.mp4", + "an_iridescent_metal_scorpion.mp4", + "An_octopus_and_a_giraffe_having_cheesecake.mp4", + "an_octopus_playing_the_harp.mp4", + "an_old_vintage_car.mp4", + "an_opulent_couch_from_the_palace_of_Versailles.mp4", + "an_orange_road_bike.mp4", + "an_orangutan_holding_a_paint_palette_in_one_hand_and_a_paintbrush_in_the_other.mp4", + "an_orangutan_playing_accordion_with_its_hands_spread_wide.mp4", + "an_orangutan_using_chopsticks_to_eat_ramen.mp4", + "an_orchid_flower_planted_in_a_clay_pot.mp4", + "a_palm_tree,_low_poly_3d_model.mp4", + "a_panda_rowing_a_boat_in_a_pond.mp4", + "a_panda_wearing_a_necktie_and_sitting_in_an_office_chair.mp4", + "A_Panther_De_Ville_car.mp4", + "a_pig_wearing_a_backpack.mp4", + "a_plate_of_delicious_tacos.mp4", + "a_plush_dragon_toy.mp4", + "a_plush_toy_of_a_corgi_nurse.mp4", + "a_rabbit,_animated_movie_character,_high_detail_3d_model.mp4", + "a_rabbit_cutting_grass_with_a_lawnmower.mp4", + "a_red_eyed_tree_frog,_low_poly.mp4", + "a_red_panda.mp4", + "a_ripe_strawberry.mp4", + "a_roulette_wheel.mp4", + "a_shiny_red_stand_mixer.mp4", + "a_silver_platter_piled_high_with_fruits.mp4", + "a_sliced_loaf_of_fresh_bread.mp4", + "a_snail_on_a_leaf.mp4", + "a_spanish_galleon_sailing_on_the_open_sea.mp4", + "a_squirrel_dressed_like_Henry_VIII_king_of_England.mp4", + "a_squirrel_gesturing_in_front_of_an_easel_showing_colorful_pie_charts.mp4", + "a_squirrel_wearing_a_tuxedo_and_holding_a_conductor's_baton.mp4", + "a_team_of_butterflies_playing_soccer_on_a_field.mp4", + "a_teddy_bear_pushing_a_shopping_cart_full_of_fruits_and_vegetables.mp4", + "a_tiger_dressed_as_a_military_general.mp4", + "a_tiger_karate_master.mp4", + "a_tiger_playing_the_violin.mp4", + "a_tiger_waiter_at_a_fancy_restaurant.mp4", + "a_tiger_wearing_a_tuxedo.mp4", + "a_t-rex_roaring_up_into_the_air.mp4", + "a_turtle_standing_on_its_hind_legs,_wearing_a_top_hat_and_holding_a_cane.mp4", + "a_typewriter.mp4", + "a_walrus_smoking_a_pipe.mp4", + "a_wedge_of_cheese_on_a_silver_platter.mp4", + "a_wide_angle_DSLR_photo_of_a_colorful_rooster.mp4", + "a_wide_angle_DSLR_photo_of_a_humanoid_banana_sitting_at_a_desk_doing_homework.mp4", + "a_wide_angle_DSLR_photo_of_a_mythical_troll_stirring_a_cauldron.mp4", + "a_wide_angle_DSLR_photo_of_a_squirrel_in_samurai_armor_wielding_a_katana.mp4", + "a_wide_angle_zoomed_out_DSLR_photo_of_A_red_dragon_dressed_in_a_tuxedo_and_playing_chess._The_chess_pieces_are_fashioned_after_robots.mp4", + "a_wide_angle_zoomed_out_DSLR_photo_of_a_skiing_penguin_wearing_a_puffy_jacket.mp4", + "a_wide_angle_zoomed_out_DSLR_photo_of_zoomed_out_view_of_Tower_Bridge_made_out_of_gingerbread_and_candy.mp4", + "a_woolly_mammoth_standing_on_ice.mp4", + "a_yellow_schoolbus.mp4", + "a_zoomed_out_DSLR_photo_of_a_3d_model_of_an_adorable_cottage_with_a_thatched_roof.mp4", + "a_zoomed_out_DSLR_photo_of_a_baby_bunny_sitting_on_top_of_a_stack_of_pancakes.mp4", + "a_zoomed_out_DSLR_photo_of_a_baby_dragon.mp4", + "a_zoomed_out_DSLR_photo_of_a_baby_monkey_riding_on_a_pig.mp4", + "a_zoomed_out_DSLR_photo_of_a_badger_wearing_a_party_hat_and_blowing_out_birthday_candles_on_a_cake.mp4", + "a_zoomed_out_DSLR_photo_of_a_beagle_eating_a_donut.mp4", + "a_zoomed_out_DSLR_photo_of_a_bear_playing_electric_bass.mp4", + "a_zoomed_out_DSLR_photo_of_a_beautifully_carved_wooden_knight_chess_piece.mp4", + "a_zoomed_out_DSLR_photo_of_a_beautiful_suit_made_out_of_moss,_on_a_mannequin._Studio_lighting,_high_quality,_high_resolution.mp4", + "a_zoomed_out_DSLR_photo_of_a_blue_lobster.mp4", + "a_zoomed_out_DSLR_photo_of_a_blue_tulip.mp4", + "a_zoomed_out_DSLR_photo_of_a_bowl_of_cereal_and_milk_with_a_spoon_in_it.mp4", + "a_zoomed_out_DSLR_photo_of_a_brain_in_a_jar.mp4", + "a_zoomed_out_DSLR_photo_of_a_bulldozer_made_out_of_toy_bricks.mp4", + "a_zoomed_out_DSLR_photo_of_a_cake_in_the_shape_of_a_train.mp4", + "a_zoomed_out_DSLR_photo_of_a_chihuahua_lying_in_a_pool_ring.mp4", + "a_zoomed_out_DSLR_photo_of_a_chimpanzee_dressed_as_a_football_player.mp4", + "a_zoomed_out_DSLR_photo_of_a_chimpanzee_holding_a_cup_of_hot_coffee.mp4", + "a_zoomed_out_DSLR_photo_of_a_chimpanzee_wearing_headphones.mp4", + "a_zoomed_out_DSLR_photo_of_a_colorful_camping_tent_in_a_patch_of_grass.mp4", + "a_zoomed_out_DSLR_photo_of_a_complex_movement_from_an_expensive_watch_with_many_shiny_gears,_sitting_on_a_table.mp4", + "a_zoomed_out_DSLR_photo_of_a_construction_excavator.mp4", + "a_zoomed_out_DSLR_photo_of_a_corgi_wearing_a_top_hat.mp4", + "a_zoomed_out_DSLR_photo_of_a_corn_cob_and_a_banana_playing_poker.mp4", + "a_zoomed_out_DSLR_photo_of_a_dachsund_riding_a_unicycle.mp4", + "a_zoomed_out_DSLR_photo_of_a_dachsund_wearing_a_boater_hat.mp4", + "a_zoomed_out_DSLR_photo_of_a_few_pool_balls_sitting_on_a_pool_table.mp4", + "a_zoomed_out_DSLR_photo_of_a_fox_working_on_a_jigsaw_puzzle.mp4", + "a_zoomed_out_DSLR_photo_of_a_fresh_cinnamon_roll_covered_in_glaze.mp4", + "a_zoomed_out_DSLR_photo_of_a_green_tractor.mp4", + "a_zoomed_out_DSLR_photo_of_a_greyhound_dog_racing_down_the_track.mp4", + "a_zoomed_out_DSLR_photo_of_a_group_of_squirrels_rowing_crew.mp4", + "a_zoomed_out_DSLR_photo_of_a_gummy_bear_driving_a_convertible.mp4", + "a_zoomed_out_DSLR_photo_of_a_hermit_crab_with_a_colorful_shell.mp4", + "a_zoomed_out_DSLR_photo_of_a_hippo_biting_through_a_watermelon.mp4", + "a_zoomed_out_DSLR_photo_of_a_hippo_made_out_of_chocolate.mp4", + "a_zoomed_out_DSLR_photo_of_a_humanoid_robot_lying_on_a_couch_using_a_laptop.mp4", + "a_zoomed_out_DSLR_photo_of_a_humanoid_robot_sitting_on_a_chair_drinking_a_cup_of_coffee.mp4", + "a_zoomed_out_DSLR_photo_of_a_human_skeleton_relaxing_in_a_lounge_chair.mp4", + "a_zoomed_out_DSLR_photo_of_a_kangaroo_sitting_on_a_bench_playing_the_accordion.mp4", + "a_zoomed_out_DSLR_photo_of_a_kingfisher_bird.mp4", + "a_zoomed_out_DSLR_photo_of_a_ladybug.mp4", + "a_zoomed_out_DSLR_photo_of_a_lion's_mane_jellyfish.mp4", + "a_zoomed_out_DSLR_photo_of_a_lobster_playing_the_saxophone.mp4", + "a_zoomed_out_DSLR_photo_of_a_majestic_sailboat.mp4", + "a_zoomed_out_DSLR_photo_of_a_marble_bust_of_a_cat,_a_real_mouse_is_sitting_on_its_head.mp4", + "a_zoomed_out_DSLR_photo_of_a_marble_bust_of_a_fox_head.mp4", + "a_zoomed_out_DSLR_photo_of_a_model_of_a_house_in_Tudor_style.mp4", + "a_zoomed_out_DSLR_photo_of_a_monkey-rabbit_hybrid.mp4", + "a_zoomed_out_DSLR_photo_of_a_monkey_riding_a_bike.mp4", + "a_zoomed_out_DSLR_photo_of_a_mountain_goat_standing_on_a_boulder.mp4", + "a_zoomed_out_DSLR_photo_of_a_mouse_holding_a_candlestick.mp4", + "a_zoomed_out_DSLR_photo_of_an_adorable_kitten_lying_next_to_a_flower.mp4", + "a_zoomed_out_DSLR_photo_of_an_all-utility_vehicle_driving_across_a_stream.mp4", + "a_zoomed_out_DSLR_photo_of_an_amigurumi_motorcycle.mp4", + "a_zoomed_out_DSLR_photo_of_an_astronaut_chopping_vegetables_in_a_sunlit_kitchen.mp4", + "a_zoomed_out_DSLR_photo_of_an_egg_cracked_open_with_a_newborn_chick_hatching_out_of_it.mp4", + "a_zoomed_out_DSLR_photo_of_an_expensive_office_chair.mp4", + "a_zoomed_out_DSLR_photo_of_an_origami_bulldozer_sitting_on_the_ground.mp4", + "a_zoomed_out_DSLR_photo_of_an_origami_crane.mp4", + "a_zoomed_out_DSLR_photo_of_an_origami_hippo_in_a_river.mp4", + "a_zoomed_out_DSLR_photo_of_an_otter_lying_on_its_back_in_the_water_holding_a_flower.mp4", + "a_zoomed_out_DSLR_photo_of_a_pair_of_floating_chopsticks_picking_up_noodles_out_of_a_bowl_of_ramen.mp4", + "a_zoomed_out_DSLR_photo_of_a_panda_throwing_wads_of_cash_into_the_air.mp4", + "a_zoomed_out_DSLR_photo_of_a_panda_wearing_a_chef's_hat_and_kneading_bread_dough_on_a_countertop.mp4", + "a_zoomed_out_DSLR_photo_of_a_pigeon_standing_on_a_manhole_cover.mp4", + "a_zoomed_out_DSLR_photo_of_a_pig_playing_the_saxophone.mp4", + "a_zoomed_out_DSLR_photo_of_a_pile_of_dice_on_a_green_tabletop.mp4", + "a_zoomed_out_DSLR_photo_of_a_pita_bread_full_of_hummus_and_falafel_and_vegetables.mp4", + "a_zoomed_out_DSLR_photo_of_a_pug_made_out_of_modeling_clay.mp4", + "a_zoomed_out_DSLR_photo_of_A_punk_rock_squirrel_in_a_studded_leather_jacket_shouting_into_a_microphone_while_standing_on_a_stump_and_holding_a_beer.mp4", + "a_zoomed_out_DSLR_photo_of_a_rabbit_cutting_grass_with_a_lawnmower.mp4", + "a_zoomed_out_DSLR_photo_of_a_rabbit_digging_a_hole_with_a_shovel.mp4", + "a_zoomed_out_DSLR_photo_of_a_raccoon_astronaut_holding_his_helmet.mp4", + "a_zoomed_out_DSLR_photo_of_a_rainforest_bird_mating_ritual_dance.mp4", + "a_zoomed_out_DSLR_photo_of_a_recliner_chair.mp4", + "a_zoomed_out_DSLR_photo_of_a_red_rotary_telephone.mp4", + "a_zoomed_out_DSLR_photo_of_a_robot_couple_fine_dining.mp4", + "a_zoomed_out_DSLR_photo_of_a_rotary_telephone_carved_out_of_wood.mp4", + "a_zoomed_out_DSLR_photo_of_a_shiny_beetle.mp4", + "a_zoomed_out_DSLR_photo_of_a_silver_candelabra_sitting_on_a_red_velvet_tablecloth,_only_one_candle_is_lit.mp4", + "a_zoomed_out_DSLR_photo_of_a_squirrel_DJing.mp4", + "a_zoomed_out_DSLR_photo_of_a_squirrel_dressed_up_like_a_Victorian_woman.mp4", + "a_zoomed_out_DSLR_photo_of_a_table_with_dim_sum_on_it.mp4", + "a_zoomed_out_DSLR_photo_of_a_tiger_dressed_as_a_maid.mp4", + "a_zoomed_out_DSLR_photo_of_a_tiger_dressed_as_a_military_general.mp4", + "a_zoomed_out_DSLR_photo_of_a_tiger_eating_an_ice_cream_cone.mp4", + "a_zoomed_out_DSLR_photo_of_a_tiger_wearing_sunglasses_and_a_leather_jacket,_riding_a_motorcycle.mp4", + "a_zoomed_out_DSLR_photo_of_a_toad_catching_a_fly_with_its_tongue.mp4", + "a_zoomed_out_DSLR_photo_of_a_wizard_raccoon_casting_a_spell.mp4", + "a_zoomed_out_DSLR_photo_of_a_yorkie_dog_dressed_as_a_maid.mp4", + "a_zoomed_out_DSLR_photo_of_cats_wearing_eyeglasses.mp4", + "a_zoomed_out_DSLR_photo_of_miniature_schnauzer_wooden_sculpture,_high_quality_studio_photo.mp4", + "A_zoomed_out_DSLR_photo_of___phoenix_made_of_splashing_water_.mp4", + "a_zoomed_out_DSLR_photo_of_Sydney_opera_house,_aerial_view.mp4", + "a_zoomed_out_DSLR_photo_of_two_foxes_tango_dancing.mp4", + "a_zoomed_out_DSLR_photo_of_two_raccoons_playing_poker.mp4", + "Chichen_Itza,_aerial_view.mp4", + "__Coffee_cup_with_many_holes.mp4", + "fries_and_a_hamburger.mp4", + "__Luminescent_wild_horses.mp4", + "Michelangelo_style_statue_of_an_astronaut.mp4", + "Michelangelo_style_statue_of_dog_reading_news_on_a_cellphone.mp4", + "the_titanic,_aerial_view.mp4", + "two_gummy_bears_playing_dominoes.mp4", + "two_macaw_parrots_playing_chess.mp4", + "Wedding_dress_made_of_tentacles.mp4", +] + + +def main(): + prompt_library = { + "dreamfusion": [ + p.replace(".mp4", "").replace("_", " ") + for p in dreamfusion_gallery_video_names + ] + } + with open("load/prompt_library.json", "w") as f: + json.dump(prompt_library, f, indent=2) + + +if __name__ == "__main__": + main() diff --git a/load/prompt_library.json b/load/prompt_library.json new file mode 100644 index 0000000..ef73987 --- /dev/null +++ b/load/prompt_library.json @@ -0,0 +1,419 @@ +{ + "dreamfusion": [ + "a 20-sided die made out of glass", + "a bald eagle carved out of wood", + "a banana peeling itself", + "a beagle in a detective's outfit", + "a beautiful dress made out of fruit, on a mannequin. Studio lighting, high quality, high resolution", + "a beautiful dress made out of garbage bags, on a mannequin. Studio lighting, high quality, high resolution", + "a beautiful rainbow fish", + "a bichon frise wearing academic regalia", + "a blue motorcycle", + "a blue poison-dart frog sitting on a water lily", + "a brightly colored mushroom growing on a log", + "a bumblebee sitting on a pink flower", + "a bunch of colorful marbles spilling out of a red velvet bag", + "a capybara wearing a top hat, low poly", + "a cat with a mullet", + "a ceramic lion", + "a ceramic upside down yellow octopus holding a blue green ceramic cup", + "a chihuahua wearing a tutu", + "a chimpanzee holding a peeled banana", + "a chimpanzee looking through a telescope", + "a chimpanzee stirring a bubbling purple potion in a cauldron", + "a chimpanzee with a big grin", + "a completely destroyed car", + "a confused beagle sitting at a desk working on homework", + "a corgi taking a selfie", + "a crab, low poly", + "a crocodile playing a drum set", + "a cute steampunk elephant", + "a dachsund dressed up in a hotdog costume", + "a delicious hamburger", + "a dragon-cat hybrid", + "a DSLR photo of a baby dragon drinking boba", + "a DSLR photo of a baby dragon hatching out of a stone egg", + "a DSLR photo of a baby grand piano viewed from far away", + "a DSLR photo of a bagel filled with cream cheese and lox", + "a DSLR photo of a bald eagle", + "a DSLR photo of a barbecue grill cooking sausages and burger patties", + "a DSLR photo of a basil plant", + "a DSLR photo of a bear dancing ballet", + "a DSLR photo of a bear dressed as a lumberjack", + "a DSLR photo of a bear dressed in medieval armor", + "a DSLR photo of a beautiful violin sitting flat on a table", + "a DSLR photo of a blue jay standing on a large basket of rainbow macarons", + "a DSLR photo of a bulldozer clearing away a pile of snow", + "a DSLR photo of a bulldozer", + "a DSLR photo of a cake covered in colorful frosting with a slice being taken out, high resolution", + "a DSLR photo of a candelabra with many candles on a red velvet tablecloth", + "a DSLR photo of a car made out of cheese", + "a DSLR photo of A car made out of sushi", + "a DSLR photo of a car made out pizza", + "a DSLR photo of a cat lying on its side batting at a ball of yarn", + "a DSLR photo of a cat magician making a white dove appear", + "a DSLR photo of a cat wearing a bee costume", + "a DSLR photo of a cat wearing a lion costume", + "a DSLR photo of a cauldron full of gold coins", + "a DSLR photo of a chimpanzee dressed like Henry VIII king of England", + "a DSLR photo of a chimpanzee dressed like Napoleon Bonaparte", + "a DSLR photo of a chow chow puppy", + "a DSLR photo of a Christmas tree with donuts as decorations", + "a DSLR photo of a chrome-plated duck with a golden beak arguing with an angry turtle in a forest", + "a DSLR photo of a classic Packard car", + "a DSLR photo of a cocker spaniel wearing a crown", + "a DSLR photo of a corgi lying on its back with its tongue lolling out", + "a DSLR photo of a corgi puppy", + "a DSLR photo of a corgi sneezing", + "a DSLR photo of a corgi standing up drinking boba", + "a DSLR photo of a corgi taking a selfie", + "a DSLR photo of a corgi wearing a beret and holding a baguette, standing up on two hind legs", + "a DSLR photo of a covered wagon", + "a DSLR photo of a cracked egg with the yolk spilling out on a wooden table", + "a DSLR photo of a cup full of pens and pencils", + "a DSLR photo of a dalmation wearing a fireman's hat", + "a DSLR photo of a delicious chocolate brownie dessert with ice cream on the side", + "a DSLR photo of a delicious croissant", + "a DSLR photo of A DMC Delorean car", + "a DSLR photo of a dog made out of salad", + "a DSLR photo of a drum set made of cheese", + "a DSLR photo of a drying rack covered in clothes", + "a DSLR photo of aerial view of a ruined castle", + "a DSLR photo of a football helmet", + "a DSLR photo of a fox holding a videogame controller", + "a DSLR photo of a fox taking a photograph using a DSLR", + "a DSLR photo of a frazer nash super sport car", + "a DSLR photo of a frog wearing a sweater", + "a DSLR photo of a ghost eating a hamburger", + "a DSLR photo of a giant worm emerging from the sand in the middle of the desert", + "a DSLR photo of a goose made out of gold", + "a DSLR photo of a green monster truck", + "a DSLR photo of a group of dogs eating pizza", + "a DSLR photo of a group of dogs playing poker", + "a DSLR photo of a gummy bear playing the saxophone", + "a DSLR photo of a hippo wearing a sweater", + "a DSLR photo of a humanoid robot holding a human brain", + "a DSLR photo of a humanoid robot playing solitaire", + "a DSLR photo of a humanoid robot playing the cello", + "a DSLR photo of a humanoid robot using a laptop", + "a DSLR photo of a humanoid robot using a rolling pin to roll out dough", + "a DSLR photo of a human skull", + "a DSLR photo of a kitten standing on top of a giant tortoise", + "a DSLR photo of a knight chopping wood", + "a DSLR photo of a knight holding a lance and sitting on an armored horse", + "a DSLR photo of a koala wearing a party hat and blowing out birthday candles on a cake", + "a DSLR photo of a lemur taking notes in a journal", + "a DSLR photo of a lion reading the newspaper", + "a DSLR photo of a mandarin duck swimming in a pond", + "a DSLR photo of a model of the eiffel tower made out of toothpicks", + "a DSLR photo of a mouse playing the tuba", + "a DSLR photo of a mug of hot chocolate with whipped cream and marshmallows", + "a DSLR photo of an adorable piglet in a field", + "a DSLR photo of an airplane taking off from the runway", + "a DSLR photo of an astronaut standing on the surface of mars", + "a DSLR photo of an eggshell broken in two with an adorable chick standing next to it", + "a DSLR photo of an elephant skull", + "a DSLR photo of an exercise bike in a well lit room", + "a DSLR photo of an extravagant mansion, aerial view", + "a DSLR photo of an ice cream sundae", + "a DSLR photo of an iguana holding a balloon", + "a DSLR photo of an intricate and complex dish from a michelin star restaurant", + "a DSLR photo of An iridescent steampunk patterned millipede with bison horns", + "a DSLR photo of an octopus playing the piano", + "a DSLR photo of an old car overgrown by vines and weeds", + "a DSLR photo of an old vintage car", + "a DSLR photo of an orangutan making a clay bowl on a throwing wheel", + "a DSLR photo of an orc forging a hammer on an anvil", + "a DSLR photo of an origami motorcycle", + "a DSLR photo of an ornate silver gravy boat sitting on a patterned tablecloth", + "a DSLR photo of an overstuffed pastrami sandwich", + "a DSLR photo of an unstable rock cairn in the middle of a stream", + "a DSLR photo of a pair of headphones sitting on a desk", + "a DSLR photo of a pair of tan cowboy boots, studio lighting, product photography", + "a DSLR photo of a peacock on a surfboard", + "a DSLR photo of a pigeon reading a book", + "a DSLR photo of a piglet sitting in a teacup", + "a DSLR photo of a pig playing a drum set", + "a DSLR photo of a pile of dice on a green tabletop next to some playing cards", + "a DSLR photo of a pirate collie dog, high resolution", + "a DSLR photo of a plate of fried chicken and waffles with maple syrup on them", + "a DSLR photo of a plate piled high with chocolate chip cookies", + "a DSLR photo of a plush t-rex dinosaur toy, studio lighting, high resolution", + "a DSLR photo of a plush triceratops toy, studio lighting, high resolution", + "a DSLR photo of a pomeranian dog", + "a DSLR photo of a porcelain dragon", + "a DSLR photo of a praying mantis wearing roller skates", + "a DSLR photo of a puffin standing on a rock", + "a DSLR photo of a pug made out of metal", + "a DSLR photo of a pug wearing a bee costume", + "a DSLR photo of a quill and ink sitting on a desk", + "a DSLR photo of a raccoon stealing a pie", + "a DSLR photo of a red cardinal bird singing", + "a DSLR photo of a red convertible car with the top down", + "a DSLR photo of a red-eyed tree frog", + "a DSLR photo of a red pickup truck driving across a stream", + "a DSLR photo of a red wheelbarrow with a shovel in it", + "a DSLR photo of a roast turkey on a platter", + "a DSLR photo of a robot and dinosaur playing chess, high resolution", + "a DSLR photo of a robot arm picking up a colorful block from a table", + "a DSLR photo of a robot cat knocking over a chess piece on a board", + "a DSLR photo of a robot dinosaur", + "a DSLR photo of a robot made out of vegetables", + "a DSLR photo of a robot stegosaurus", + "a DSLR photo of a robot tiger", + "a DSLR photo of a rolling pin on top of bread dough", + "a DSLR photo of a sheepdog running", + "a DSLR photo of a shiba inu playing golf wearing tartan golf clothes and hat", + "a DSLR photo of a shiny silver robot cat", + "a DSLR photo of a silverback gorilla holding a golden trophy", + "a DSLR photo of a silver humanoid robot flipping a coin", + "a DSLR photo of a small cherry tomato plant in a pot with a few red tomatoes growing on it", + "a DSLR photo of a small saguaro cactus planted in a clay pot", + "a DSLR photo of a Space Shuttle", + "a DSLR photo of a squirrel dressed like a clown", + "a DSLR photo of a squirrel flying a biplane", + "a DSLR photo of a squirrel giving a lecture writing on a chalkboard", + "a DSLR photo of a squirrel holding a bowling ball", + "a DSLR photo of a squirrel-lizard hybrid", + "a DSLR photo of a squirrel made out of fruit", + "a DSLR photo of a squirrel-octopus hybrid", + "a DSLR photo of a stack of pancakes covered in maple syrup", + "a DSLR photo of a steam engine train, high resolution", + "a DSLR photo of a steaming basket full of dumplings", + "a DSLR photo of a steaming hot plate piled high with spaghetti and meatballs", + "a DSLR photo of a steampunk space ship designed in the 18th century", + "a DSLR photo of a straw basket with a cobra coming out of it", + "a DSLR photo of a swan and its cygnets swimming in a pond", + "a DSLR photo of a tarantula, highly detailed", + "a DSLR photo of a teal moped", + "a DSLR photo of a teapot shaped like an elephant head where its snout acts as the spout", + "a DSLR photo of a teddy bear taking a selfie", + "a DSLR photo of a terracotta bunny", + "a DSLR photo of a tiger dressed as a doctor", + "a DSLR photo of a tiger made out of yarn", + "a DSLR photo of a toilet made out of gold", + "a DSLR photo of a toy robot", + "a DSLR photo of a train engine made out of clay", + "a DSLR photo of a tray of Sushi containing pugs", + "a DSLR photo of a tree stump with an axe buried in it", + "a DSLR photo of a turtle standing on its hind legs, wearing a top hat and holding a cane", + "a DSLR photo of a very beautiful small organic sculpture made of fine clockwork and gears with tiny ruby bearings, very intricate, caved, curved. Studio lighting, High resolution, white background", + "a DSLR photo of A very beautiful tiny human heart organic sculpture made of copper wire and threaded pipes, very intricate, curved, Studio lighting, high resolution", + "a DSLR photo of a very cool and trendy pair of sneakers, studio lighting", + "a DSLR photo of a vintage record player", + "a DSLR photo of a wine bottle and full wine glass on a chessboard", + "a DSLR photo of a wooden desk and chair from an elementary school", + "a DSLR photo of a yorkie dog eating a donut", + "a DSLR photo of a yorkie dog wearing extremely cool sneakers", + "a DSLR photo of baby elephant jumping on a trampoline", + "a DSLR photo of cat wearing virtual reality headset in renaissance oil painting high detail caravaggio", + "a DSLR photo of edible typewriter made out of vegetables", + "a DSLR photo of Mont Saint-Michel, France, aerial view", + "a DSLR photo of Mount Fuji, aerial view", + "a DSLR photo of Neuschwanstein Castle, aerial view", + "A DSLR photo of pyramid shaped burrito with a slice cut out of it", + "a DSLR photo of the Imperial State Crown of England", + "a DSLR photo of the leaning tower of Pisa, aerial view", + "a DSLR photo of the Statue of Liberty, aerial view", + "a DSLR photo of Two locomotives playing tug of war", + "a DSLR photo of two macaw parrots sharing a milkshake with two straws", + "a DSLR photo of Westminster Abbey, aerial view", + "a ficus planted in a pot", + "a flower made out of metal", + "a fluffy cat lying on its back in a patch of sunlight", + "a fox and a hare tangoing together", + "a fox holding a videogame controller", + "a fox playing the cello", + "a frazer nash super sport car", + "a freshly baked loaf of sourdough bread on a cutting board", + "a goat drinking beer", + "a golden goblet, low poly", + "a green dragon breathing fire", + "a green tractor farming corn fields", + "a highland cow", + "a hotdog in a tutu skirt", + "a humanoid robot laying on the couch while on a laptop", + "a humanoid robot playing the violin", + "a humanoid robot sitting looking at a Go board with some pieces on it", + "a human skeleton drinking a glass of red wine", + "a human skull with a vine growing through one of the eye sockets", + "a kitten looking at a goldfish in a bowl", + "a lemur drinking boba", + "a lemur taking notes in a journal", + "a lionfish", + "a llama wearing a suit", + "a marble bust of a mouse", + "a metal sculpture of a lion's head, highly detailed", + "a mojito in a beach chair", + "a monkey-rabbit hybrid", + "an airplane made out of wood", + "an amigurumi bulldozer", + "An anthropomorphic tomato eating another tomato", + "an astronaut playing the violin", + "an astronaut riding a kangaroo", + "an English castle, aerial view", + "an erupting volcano, aerial view", + "a nest with a few white eggs and one golden egg", + "an exercise bike", + "an iridescent metal scorpion", + "An octopus and a giraffe having cheesecake", + "an octopus playing the harp", + "an old vintage car", + "an opulent couch from the palace of Versailles", + "an orange road bike", + "an orangutan holding a paint palette in one hand and a paintbrush in the other", + "an orangutan playing accordion with its hands spread wide", + "an orangutan using chopsticks to eat ramen", + "an orchid flower planted in a clay pot", + "a palm tree, low poly 3d model", + "a panda rowing a boat in a pond", + "a panda wearing a necktie and sitting in an office chair", + "A Panther De Ville car", + "a pig wearing a backpack", + "a plate of delicious tacos", + "a plush dragon toy", + "a plush toy of a corgi nurse", + "a rabbit, animated movie character, high detail 3d model", + "a rabbit cutting grass with a lawnmower", + "a red eyed tree frog, low poly", + "a red panda", + "a ripe strawberry", + "a roulette wheel", + "a shiny red stand mixer", + "a silver platter piled high with fruits", + "a sliced loaf of fresh bread", + "a snail on a leaf", + "a spanish galleon sailing on the open sea", + "a squirrel dressed like Henry VIII king of England", + "a squirrel gesturing in front of an easel showing colorful pie charts", + "a squirrel wearing a tuxedo and holding a conductor's baton", + "a team of butterflies playing soccer on a field", + "a teddy bear pushing a shopping cart full of fruits and vegetables", + "a tiger dressed as a military general", + "a tiger karate master", + "a tiger playing the violin", + "a tiger waiter at a fancy restaurant", + "a tiger wearing a tuxedo", + "a t-rex roaring up into the air", + "a turtle standing on its hind legs, wearing a top hat and holding a cane", + "a typewriter", + "a walrus smoking a pipe", + "a wedge of cheese on a silver platter", + "a wide angle DSLR photo of a colorful rooster", + "a wide angle DSLR photo of a humanoid banana sitting at a desk doing homework", + "a wide angle DSLR photo of a mythical troll stirring a cauldron", + "a wide angle DSLR photo of a squirrel in samurai armor wielding a katana", + "a wide angle zoomed out DSLR photo of A red dragon dressed in a tuxedo and playing chess. The chess pieces are fashioned after robots", + "a wide angle zoomed out DSLR photo of a skiing penguin wearing a puffy jacket", + "a wide angle zoomed out DSLR photo of zoomed out view of Tower Bridge made out of gingerbread and candy", + "a woolly mammoth standing on ice", + "a yellow schoolbus", + "a zoomed out DSLR photo of a 3d model of an adorable cottage with a thatched roof", + "a zoomed out DSLR photo of a baby bunny sitting on top of a stack of pancakes", + "a zoomed out DSLR photo of a baby dragon", + "a zoomed out DSLR photo of a baby monkey riding on a pig", + "a zoomed out DSLR photo of a badger wearing a party hat and blowing out birthday candles on a cake", + "a zoomed out DSLR photo of a beagle eating a donut", + "a zoomed out DSLR photo of a bear playing electric bass", + "a zoomed out DSLR photo of a beautifully carved wooden knight chess piece", + "a zoomed out DSLR photo of a beautiful suit made out of moss, on a mannequin. Studio lighting, high quality, high resolution", + "a zoomed out DSLR photo of a blue lobster", + "a zoomed out DSLR photo of a blue tulip", + "a zoomed out DSLR photo of a bowl of cereal and milk with a spoon in it", + "a zoomed out DSLR photo of a brain in a jar", + "a zoomed out DSLR photo of a bulldozer made out of toy bricks", + "a zoomed out DSLR photo of a cake in the shape of a train", + "a zoomed out DSLR photo of a chihuahua lying in a pool ring", + "a zoomed out DSLR photo of a chimpanzee dressed as a football player", + "a zoomed out DSLR photo of a chimpanzee holding a cup of hot coffee", + "a zoomed out DSLR photo of a chimpanzee wearing headphones", + "a zoomed out DSLR photo of a colorful camping tent in a patch of grass", + "a zoomed out DSLR photo of a complex movement from an expensive watch with many shiny gears, sitting on a table", + "a zoomed out DSLR photo of a construction excavator", + "a zoomed out DSLR photo of a corgi wearing a top hat", + "a zoomed out DSLR photo of a corn cob and a banana playing poker", + "a zoomed out DSLR photo of a dachsund riding a unicycle", + "a zoomed out DSLR photo of a dachsund wearing a boater hat", + "a zoomed out DSLR photo of a few pool balls sitting on a pool table", + "a zoomed out DSLR photo of a fox working on a jigsaw puzzle", + "a zoomed out DSLR photo of a fresh cinnamon roll covered in glaze", + "a zoomed out DSLR photo of a green tractor", + "a zoomed out DSLR photo of a greyhound dog racing down the track", + "a zoomed out DSLR photo of a group of squirrels rowing crew", + "a zoomed out DSLR photo of a gummy bear driving a convertible", + "a zoomed out DSLR photo of a hermit crab with a colorful shell", + "a zoomed out DSLR photo of a hippo biting through a watermelon", + "a zoomed out DSLR photo of a hippo made out of chocolate", + "a zoomed out DSLR photo of a humanoid robot lying on a couch using a laptop", + "a zoomed out DSLR photo of a humanoid robot sitting on a chair drinking a cup of coffee", + "a zoomed out DSLR photo of a human skeleton relaxing in a lounge chair", + "a zoomed out DSLR photo of a kangaroo sitting on a bench playing the accordion", + "a zoomed out DSLR photo of a kingfisher bird", + "a zoomed out DSLR photo of a ladybug", + "a zoomed out DSLR photo of a lion's mane jellyfish", + "a zoomed out DSLR photo of a lobster playing the saxophone", + "a zoomed out DSLR photo of a majestic sailboat", + "a zoomed out DSLR photo of a marble bust of a cat, a real mouse is sitting on its head", + "a zoomed out DSLR photo of a marble bust of a fox head", + "a zoomed out DSLR photo of a model of a house in Tudor style", + "a zoomed out DSLR photo of a monkey-rabbit hybrid", + "a zoomed out DSLR photo of a monkey riding a bike", + "a zoomed out DSLR photo of a mountain goat standing on a boulder", + "a zoomed out DSLR photo of a mouse holding a candlestick", + "a zoomed out DSLR photo of an adorable kitten lying next to a flower", + "a zoomed out DSLR photo of an all-utility vehicle driving across a stream", + "a zoomed out DSLR photo of an amigurumi motorcycle", + "a zoomed out DSLR photo of an astronaut chopping vegetables in a sunlit kitchen", + "a zoomed out DSLR photo of an egg cracked open with a newborn chick hatching out of it", + "a zoomed out DSLR photo of an expensive office chair", + "a zoomed out DSLR photo of an origami bulldozer sitting on the ground", + "a zoomed out DSLR photo of an origami crane", + "a zoomed out DSLR photo of an origami hippo in a river", + "a zoomed out DSLR photo of an otter lying on its back in the water holding a flower", + "a zoomed out DSLR photo of a pair of floating chopsticks picking up noodles out of a bowl of ramen", + "a zoomed out DSLR photo of a panda throwing wads of cash into the air", + "a zoomed out DSLR photo of a panda wearing a chef's hat and kneading bread dough on a countertop", + "a zoomed out DSLR photo of a pigeon standing on a manhole cover", + "a zoomed out DSLR photo of a pig playing the saxophone", + "a zoomed out DSLR photo of a pile of dice on a green tabletop", + "a zoomed out DSLR photo of a pita bread full of hummus and falafel and vegetables", + "a zoomed out DSLR photo of a pug made out of modeling clay", + "a zoomed out DSLR photo of A punk rock squirrel in a studded leather jacket shouting into a microphone while standing on a stump and holding a beer", + "a zoomed out DSLR photo of a rabbit cutting grass with a lawnmower", + "a zoomed out DSLR photo of a rabbit digging a hole with a shovel", + "a zoomed out DSLR photo of a raccoon astronaut holding his helmet", + "a zoomed out DSLR photo of a rainforest bird mating ritual dance", + "a zoomed out DSLR photo of a recliner chair", + "a zoomed out DSLR photo of a red rotary telephone", + "a zoomed out DSLR photo of a robot couple fine dining", + "a zoomed out DSLR photo of a rotary telephone carved out of wood", + "a zoomed out DSLR photo of a shiny beetle", + "a zoomed out DSLR photo of a silver candelabra sitting on a red velvet tablecloth, only one candle is lit", + "a zoomed out DSLR photo of a squirrel DJing", + "a zoomed out DSLR photo of a squirrel dressed up like a Victorian woman", + "a zoomed out DSLR photo of a table with dim sum on it", + "a zoomed out DSLR photo of a tiger dressed as a maid", + "a zoomed out DSLR photo of a tiger dressed as a military general", + "a zoomed out DSLR photo of a tiger eating an ice cream cone", + "a zoomed out DSLR photo of a tiger wearing sunglasses and a leather jacket, riding a motorcycle", + "a zoomed out DSLR photo of a toad catching a fly with its tongue", + "a zoomed out DSLR photo of a wizard raccoon casting a spell", + "a zoomed out DSLR photo of a yorkie dog dressed as a maid", + "a zoomed out DSLR photo of cats wearing eyeglasses", + "a zoomed out DSLR photo of miniature schnauzer wooden sculpture, high quality studio photo", + "A zoomed out DSLR photo of phoenix made of splashing water ", + "a zoomed out DSLR photo of Sydney opera house, aerial view", + "a zoomed out DSLR photo of two foxes tango dancing", + "a zoomed out DSLR photo of two raccoons playing poker", + "Chichen Itza, aerial view", + " Coffee cup with many holes", + "fries and a hamburger", + " Luminescent wild horses", + "Michelangelo style statue of an astronaut", + "Michelangelo style statue of dog reading news on a cellphone", + "the titanic, aerial view", + "two gummy bears playing dominoes", + "two macaw parrots playing chess", + "Wedding dress made of tentacles" + ] +} diff --git a/load/shapes/README.md b/load/shapes/README.md new file mode 100644 index 0000000..f886f1f --- /dev/null +++ b/load/shapes/README.md @@ -0,0 +1,10 @@ +# Shape Credits + +- `animal.obj` - Ido Richardson +- `hand_prismatic.obj` - Ido Richardson +- `potion.obj` - Ido Richardson +- `blub.obj` - [Keenan's 3D Model Repository](https://www.cs.cmu.edu/~kmcrane/Projects/ModelRepository/) +- `nascar.obj` - [Princeton ModelNet](https://modelnet.cs.princeton.edu/) +- `cabin.obj` - [Princeton ModelNet](https://modelnet.cs.princeton.edu/) +- `teddy.obj` - [Gal Metzer](https://galmetzer.github.io/) +- `human.obj` - [TurboSquid](https://www.turbosquid.com/3d-models/3d-model-character-base/524860) diff --git a/load/shapes/animal.obj b/load/shapes/animal.obj new file mode 100644 index 0000000..9d4e199 --- /dev/null +++ b/load/shapes/animal.obj @@ -0,0 +1,8909 @@ +#### +# +# OBJ File Generated by Meshlab +# +#### +# Object animal_legs_head.obj +# +# Vertices: 1536 +# Faces: 3068 +# +#### +mtllib ./animal_legs_head.obj.mtl + +vn -0.037566 0.880458 -0.472633 +v 9.999994 49.041206 -42.944695 +vn -0.715259 0.532976 -0.452042 +v 9.999994 48.646877 -43.444782 +vn -0.716542 0.495637 -0.490828 +v 9.999994 48.261967 -43.867054 +vn -0.717102 0.451769 -0.530726 +v 9.999994 47.874546 -44.227222 +vn -0.717793 0.401355 -0.568935 +v 9.999994 47.469975 -44.542099 +vn -0.718191 0.344641 -0.604504 +v 9.999994 47.026360 -44.824703 +vn -0.718058 0.283944 -0.635428 +v 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2886/1100/2952 2891/579/2952 2892/583/2952 +f 2880/1104/2953 2879/1102/2953 2417/581/2953 +f 2880/1104/2956 2418/585/2956 2419/560/2956 +f 2874/1109/2957 2873/1106/2957 2411/586/2957 +f 2881/1108/2958 2419/560/2958 2888/562/2958 diff --git a/load/tets/128_tets.npz b/load/tets/128_tets.npz new file mode 100644 index 0000000..2ea2f1d Binary files /dev/null and b/load/tets/128_tets.npz differ diff --git a/load/tets/32_tets.npz b/load/tets/32_tets.npz new file mode 100644 index 0000000..54f7ea5 Binary files /dev/null and b/load/tets/32_tets.npz differ diff --git a/load/tets/64_tets.npz b/load/tets/64_tets.npz new file mode 100644 index 0000000..ec37c89 Binary files /dev/null and b/load/tets/64_tets.npz differ diff --git a/load/tets/generate_tets.py b/load/tets/generate_tets.py new file mode 100644 index 0000000..424d852 --- /dev/null +++ b/load/tets/generate_tets.py @@ -0,0 +1,58 @@ +# Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +# property and proprietary rights in and to this material, related +# documentation and any modifications thereto. Any use, reproduction, +# disclosure or distribution of this material and related documentation +# without an express license agreement from NVIDIA CORPORATION or +# its affiliates is strictly prohibited. + +import os + +import numpy as np + +""" +This code segment shows how to use Quartet: https://github.com/crawforddoran/quartet, +to generate a tet grid +1) Download, compile and run Quartet as described in the link above. Example usage `quartet meshes/cube.obj 0.5 cube_5.tet` +2) Run the function below to generate a file `cube_32_tet.tet` +""" + + +def generate_tetrahedron_grid_file(res=32, root=".."): + frac = 1.0 / res + command = f"cd {root}; ./quartet meshes/cube.obj {frac} meshes/cube_{res}_tet.tet -s meshes/cube_boundary_{res}.obj" + os.system(command) + + +""" +This code segment shows how to convert from a quartet .tet file to compressed npz file +""" + + +def convert_from_quartet_to_npz(quartetfile="cube_32_tet.tet", npzfile="32_tets"): + file1 = open(quartetfile, "r") + header = file1.readline() + numvertices = int(header.split(" ")[1]) + numtets = int(header.split(" ")[2]) + print(numvertices, numtets) + + # load vertices + vertices = np.loadtxt(quartetfile, skiprows=1, max_rows=numvertices) + print(vertices.shape) + + # load indices + indices = np.loadtxt( + quartetfile, dtype=int, skiprows=1 + numvertices, max_rows=numtets + ) + print(indices.shape) + + np.savez_compressed(npzfile, vertices=vertices, indices=indices) + + +root = "/home/gyc/quartet" +for res in [300, 350, 400]: + generate_tetrahedron_grid_file(res, root) + convert_from_quartet_to_npz( + os.path.join(root, f"meshes/cube_{res}_tet.tet"), npzfile=f"{res}_tets" + ) diff --git a/load/zero123/download.sh b/load/zero123/download.sh new file mode 100644 index 0000000..169676b --- /dev/null +++ b/load/zero123/download.sh @@ -0,0 +1,4 @@ +# wget https://huggingface.co/cvlab/zero123-weights/resolve/main/105000.ckpt +# mv 105000.ckpt zero123-original.ckpt +wget https://zero123.cs.columbia.edu/assets/zero123-xl.ckpt +# Download stable_zero123.ckpt from https://huggingface.co/stabilityai/stable-zero123 diff --git a/load/zero123/sd-objaverse-finetune-c_concat-256.yaml b/load/zero123/sd-objaverse-finetune-c_concat-256.yaml new file mode 100755 index 0000000..f139178 --- /dev/null +++ b/load/zero123/sd-objaverse-finetune-c_concat-256.yaml @@ -0,0 +1,118 @@ +model: + base_learning_rate: 1.0e-04 + target: extern.ldm_zero123.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "image_target" + cond_stage_key: "image_cond" + image_size: 32 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: hybrid + monitor: val/loss_simple_ema + scale_factor: 0.18215 + + scheduler_config: # 10000 warmup steps + target: extern.ldm_zero123.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 100 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: extern.ldm_zero123.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 8 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + use_fp16: True + + first_stage_config: + target: extern.ldm_zero123.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: extern.ldm_zero123.modules.encoders.modules.FrozenCLIPImageEmbedder + + +# data: +# target: extern.ldm_zero123.data.simple.ObjaverseDataModuleFromConfig +# params: +# root_dir: 'views_whole_sphere' +# batch_size: 192 +# num_workers: 16 +# total_view: 4 +# train: +# validation: False +# image_transforms: +# size: 256 + +# validation: +# validation: True +# image_transforms: +# size: 256 + + +# lightning: +# find_unused_parameters: false +# metrics_over_trainsteps_checkpoint: True +# modelcheckpoint: +# params: +# every_n_train_steps: 5000 +# callbacks: +# image_logger: +# target: main.ImageLogger +# params: +# batch_frequency: 500 +# max_images: 32 +# increase_log_steps: False +# log_first_step: True +# log_images_kwargs: +# use_ema_scope: False +# inpaint: False +# plot_progressive_rows: False +# plot_diffusion_rows: False +# N: 32 +# unconditional_scale: 3.0 +# unconditional_label: [""] + +# trainer: +# benchmark: True +# val_check_interval: 5000000 # really sorry +# num_sanity_val_steps: 0 +# accumulate_grad_batches: 1 diff --git a/requirements-dev.txt b/requirements-dev.txt new file mode 100644 index 0000000..9bae3fb --- /dev/null +++ b/requirements-dev.txt @@ -0,0 +1,4 @@ +black +mypy +pylint +pre-commit diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..da66704 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,51 @@ +lightning==2.0.0 +omegaconf==2.3.0 +jaxtyping +typeguard +git+https://github.com/KAIR-BAIR/nerfacc.git@v0.5.2 +git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch +diffusers<0.20 +transformers==4.28.1 +accelerate +opencv-python +tensorboard +matplotlib +imageio>=2.28.0 +imageio[ffmpeg] +git+https://github.com/NVlabs/nvdiffrast.git +libigl +xatlas +trimesh[easy] +networkx +pysdf +PyMCubes +wandb +gradio==4.11.0 +git+https://github.com/ashawkey/envlight.git +torchmetrics + +# deepfloyd +xformers +bitsandbytes==0.38.1 +sentencepiece +safetensors +huggingface_hub + +# for zero123 +einops +kornia +taming-transformers-rom1504 +git+https://github.com/openai/CLIP.git + +#controlnet +controlnet_aux + + +# DreamMesh4D +pypose==0.6.7 +git+https://github.com/facebookresearch/pytorch3d.git@stable +easydict +potpourri3d +diff-gaussian-rasterization./ +simple-knn./ +git+https://github.com/KinglittleQ/torch-batch-svd.git \ No newline at end of file diff --git a/scripts/convert_zero123_to_diffusers.py b/scripts/convert_zero123_to_diffusers.py new file mode 100644 index 0000000..e212774 --- /dev/null +++ b/scripts/convert_zero123_to_diffusers.py @@ -0,0 +1,1025 @@ +import argparse +import sys + +import torch +from diffusers.models import AutoencoderKL, UNet2DConditionModel +from diffusers.schedulers import DDIMScheduler +from diffusers.utils import logging +from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection + +sys.path.append("extern/") +from accelerate import init_empty_weights +from accelerate.utils import set_module_tensor_to_device +from zero123 import CLIPCameraProjection, Zero123Pipeline + +logger = logging.get_logger(__name__) + + +def create_unet_diffusers_config(original_config, image_size: int, controlnet=False): + """ + Creates a config for the diffusers based on the config of the LDM model. + """ + if controlnet: + unet_params = original_config.model.params.control_stage_config.params + else: + if ( + "unet_config" in original_config.model.params + and original_config.model.params.unet_config is not None + ): + unet_params = original_config.model.params.unet_config.params + else: + unet_params = original_config.model.params.network_config.params + + vae_params = original_config.model.params.first_stage_config.params.ddconfig + + block_out_channels = [ + unet_params.model_channels * mult for mult in unet_params.channel_mult + ] + + down_block_types = [] + resolution = 1 + for i in range(len(block_out_channels)): + block_type = ( + "CrossAttnDownBlock2D" + if resolution in unet_params.attention_resolutions + else "DownBlock2D" + ) + down_block_types.append(block_type) + if i != len(block_out_channels) - 1: + resolution *= 2 + + up_block_types = [] + for i in range(len(block_out_channels)): + block_type = ( + "CrossAttnUpBlock2D" + if resolution in unet_params.attention_resolutions + else "UpBlock2D" + ) + up_block_types.append(block_type) + resolution //= 2 + + if unet_params.transformer_depth is not None: + transformer_layers_per_block = ( + unet_params.transformer_depth + if isinstance(unet_params.transformer_depth, int) + else list(unet_params.transformer_depth) + ) + else: + transformer_layers_per_block = 1 + + vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1) + + head_dim = unet_params.num_heads if "num_heads" in unet_params else None + use_linear_projection = ( + unet_params.use_linear_in_transformer + if "use_linear_in_transformer" in unet_params + else False + ) + if use_linear_projection: + # stable diffusion 2-base-512 and 2-768 + if head_dim is None: + head_dim_mult = unet_params.model_channels // unet_params.num_head_channels + head_dim = [head_dim_mult * c for c in list(unet_params.channel_mult)] + + class_embed_type = None + addition_embed_type = None + addition_time_embed_dim = None + projection_class_embeddings_input_dim = None + context_dim = None + + if unet_params.context_dim is not None: + context_dim = ( + unet_params.context_dim + if isinstance(unet_params.context_dim, int) + else unet_params.context_dim[0] + ) + + if "num_classes" in unet_params: + if unet_params.num_classes == "sequential": + if context_dim in [2048, 1280]: + # SDXL + addition_embed_type = "text_time" + addition_time_embed_dim = 256 + else: + class_embed_type = "projection" + assert "adm_in_channels" in unet_params + projection_class_embeddings_input_dim = unet_params.adm_in_channels + else: + raise NotImplementedError( + f"Unknown conditional unet num_classes config: {unet_params.num_classes}" + ) + + config = { + "sample_size": image_size // vae_scale_factor, + "in_channels": unet_params.in_channels, + "down_block_types": tuple(down_block_types), + "block_out_channels": tuple(block_out_channels), + "layers_per_block": unet_params.num_res_blocks, + "cross_attention_dim": context_dim, + "attention_head_dim": head_dim, + "use_linear_projection": use_linear_projection, + "class_embed_type": class_embed_type, + "addition_embed_type": addition_embed_type, + "addition_time_embed_dim": addition_time_embed_dim, + "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, + "transformer_layers_per_block": transformer_layers_per_block, + } + + if controlnet: + config["conditioning_channels"] = unet_params.hint_channels + else: + config["out_channels"] = unet_params.out_channels + config["up_block_types"] = tuple(up_block_types) + + return config + + +def assign_to_checkpoint( + paths, + checkpoint, + old_checkpoint, + attention_paths_to_split=None, + additional_replacements=None, + config=None, +): + """ + This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits + attention layers, and takes into account additional replacements that may arise. + + Assigns the weights to the new checkpoint. + """ + assert isinstance( + paths, list + ), "Paths should be a list of dicts containing 'old' and 'new' keys." + + # Splits the attention layers into three variables. + if attention_paths_to_split is not None: + for path, path_map in attention_paths_to_split.items(): + old_tensor = old_checkpoint[path] + channels = old_tensor.shape[0] // 3 + + target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) + + num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 + + old_tensor = old_tensor.reshape( + (num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] + ) + query, key, value = old_tensor.split(channels // num_heads, dim=1) + + checkpoint[path_map["query"]] = query.reshape(target_shape) + checkpoint[path_map["key"]] = key.reshape(target_shape) + checkpoint[path_map["value"]] = value.reshape(target_shape) + + for path in paths: + new_path = path["new"] + + # These have already been assigned + if ( + attention_paths_to_split is not None + and new_path in attention_paths_to_split + ): + continue + + # Global renaming happens here + new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") + new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") + new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") + + if additional_replacements is not None: + for replacement in additional_replacements: + new_path = new_path.replace(replacement["old"], replacement["new"]) + + # proj_attn.weight has to be converted from conv 1D to linear + is_attn_weight = "proj_attn.weight" in new_path or ( + "attentions" in new_path and "to_" in new_path + ) + shape = old_checkpoint[path["old"]].shape + if is_attn_weight and len(shape) == 3: + checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] + elif is_attn_weight and len(shape) == 4: + checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0] + else: + checkpoint[new_path] = old_checkpoint[path["old"]] + + +def shave_segments(path, n_shave_prefix_segments=1): + """ + Removes segments. Positive values shave the first segments, negative shave the last segments. + """ + if n_shave_prefix_segments >= 0: + return ".".join(path.split(".")[n_shave_prefix_segments:]) + else: + return ".".join(path.split(".")[:n_shave_prefix_segments]) + + +def renew_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item.replace("in_layers.0", "norm1") + new_item = new_item.replace("in_layers.2", "conv1") + + new_item = new_item.replace("out_layers.0", "norm2") + new_item = new_item.replace("out_layers.3", "conv2") + + new_item = new_item.replace("emb_layers.1", "time_emb_proj") + new_item = new_item.replace("skip_connection", "conv_shortcut") + + new_item = shave_segments( + new_item, n_shave_prefix_segments=n_shave_prefix_segments + ) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + # new_item = new_item.replace('norm.weight', 'group_norm.weight') + # new_item = new_item.replace('norm.bias', 'group_norm.bias') + + # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') + # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') + + # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def convert_ldm_unet_checkpoint( + checkpoint, + config, + path=None, + extract_ema=False, + controlnet=False, + skip_extract_state_dict=False, +): + """ + Takes a state dict and a config, and returns a converted checkpoint. + """ + + if skip_extract_state_dict: + unet_state_dict = checkpoint + else: + # extract state_dict for UNet + unet_state_dict = {} + keys = list(checkpoint.keys()) + + if controlnet: + unet_key = "control_model." + else: + unet_key = "model.diffusion_model." + + # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA + if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: + logger.warning(f"Checkpoint {path} has both EMA and non-EMA weights.") + logger.warning( + "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" + " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." + ) + for key in keys: + if key.startswith("model.diffusion_model"): + flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) + unet_state_dict[key.replace(unet_key, "")] = checkpoint[ + flat_ema_key + ] + else: + if sum(k.startswith("model_ema") for k in keys) > 100: + logger.warning( + "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" + " weights (usually better for inference), please make sure to add the `--extract_ema` flag." + ) + + for key in keys: + if key.startswith(unet_key): + unet_state_dict[key.replace(unet_key, "")] = checkpoint[key] + + new_checkpoint = {} + + new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict[ + "time_embed.0.weight" + ] + new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict[ + "time_embed.0.bias" + ] + new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict[ + "time_embed.2.weight" + ] + new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict[ + "time_embed.2.bias" + ] + + if config["class_embed_type"] is None: + # No parameters to port + ... + elif ( + config["class_embed_type"] == "timestep" + or config["class_embed_type"] == "projection" + ): + new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict[ + "label_emb.0.0.weight" + ] + new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict[ + "label_emb.0.0.bias" + ] + new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict[ + "label_emb.0.2.weight" + ] + new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict[ + "label_emb.0.2.bias" + ] + else: + raise NotImplementedError( + f"Not implemented `class_embed_type`: {config['class_embed_type']}" + ) + + if config["addition_embed_type"] == "text_time": + new_checkpoint["add_embedding.linear_1.weight"] = unet_state_dict[ + "label_emb.0.0.weight" + ] + new_checkpoint["add_embedding.linear_1.bias"] = unet_state_dict[ + "label_emb.0.0.bias" + ] + new_checkpoint["add_embedding.linear_2.weight"] = unet_state_dict[ + "label_emb.0.2.weight" + ] + new_checkpoint["add_embedding.linear_2.bias"] = unet_state_dict[ + "label_emb.0.2.bias" + ] + + new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] + new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] + + if not controlnet: + new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] + new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] + new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] + new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] + + # Retrieves the keys for the input blocks only + num_input_blocks = len( + { + ".".join(layer.split(".")[:2]) + for layer in unet_state_dict + if "input_blocks" in layer + } + ) + input_blocks = { + layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] + for layer_id in range(num_input_blocks) + } + + # Retrieves the keys for the middle blocks only + num_middle_blocks = len( + { + ".".join(layer.split(".")[:2]) + for layer in unet_state_dict + if "middle_block" in layer + } + ) + middle_blocks = { + layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] + for layer_id in range(num_middle_blocks) + } + + # Retrieves the keys for the output blocks only + num_output_blocks = len( + { + ".".join(layer.split(".")[:2]) + for layer in unet_state_dict + if "output_blocks" in layer + } + ) + output_blocks = { + layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] + for layer_id in range(num_output_blocks) + } + + for i in range(1, num_input_blocks): + block_id = (i - 1) // (config["layers_per_block"] + 1) + layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) + + resnets = [ + key + for key in input_blocks[i] + if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key + ] + attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] + + if f"input_blocks.{i}.0.op.weight" in unet_state_dict: + new_checkpoint[ + f"down_blocks.{block_id}.downsamplers.0.conv.weight" + ] = unet_state_dict.pop(f"input_blocks.{i}.0.op.weight") + new_checkpoint[ + f"down_blocks.{block_id}.downsamplers.0.conv.bias" + ] = unet_state_dict.pop(f"input_blocks.{i}.0.op.bias") + + paths = renew_resnet_paths(resnets) + meta_path = { + "old": f"input_blocks.{i}.0", + "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, + new_checkpoint, + unet_state_dict, + additional_replacements=[meta_path], + config=config, + ) + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = { + "old": f"input_blocks.{i}.1", + "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, + new_checkpoint, + unet_state_dict, + additional_replacements=[meta_path], + config=config, + ) + + resnet_0 = middle_blocks[0] + attentions = middle_blocks[1] + resnet_1 = middle_blocks[2] + + resnet_0_paths = renew_resnet_paths(resnet_0) + assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) + + resnet_1_paths = renew_resnet_paths(resnet_1) + assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) + + attentions_paths = renew_attention_paths(attentions) + meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} + assign_to_checkpoint( + attentions_paths, + new_checkpoint, + unet_state_dict, + additional_replacements=[meta_path], + config=config, + ) + + for i in range(num_output_blocks): + block_id = i // (config["layers_per_block"] + 1) + layer_in_block_id = i % (config["layers_per_block"] + 1) + output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] + output_block_list = {} + + for layer in output_block_layers: + layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) + if layer_id in output_block_list: + output_block_list[layer_id].append(layer_name) + else: + output_block_list[layer_id] = [layer_name] + + if len(output_block_list) > 1: + resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] + attentions = [ + key for key in output_blocks[i] if f"output_blocks.{i}.1" in key + ] + + resnet_0_paths = renew_resnet_paths(resnets) + paths = renew_resnet_paths(resnets) + + meta_path = { + "old": f"output_blocks.{i}.0", + "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, + new_checkpoint, + unet_state_dict, + additional_replacements=[meta_path], + config=config, + ) + + output_block_list = {k: sorted(v) for k, v in output_block_list.items()} + if ["conv.bias", "conv.weight"] in output_block_list.values(): + index = list(output_block_list.values()).index( + ["conv.bias", "conv.weight"] + ) + new_checkpoint[ + f"up_blocks.{block_id}.upsamplers.0.conv.weight" + ] = unet_state_dict[f"output_blocks.{i}.{index}.conv.weight"] + new_checkpoint[ + f"up_blocks.{block_id}.upsamplers.0.conv.bias" + ] = unet_state_dict[f"output_blocks.{i}.{index}.conv.bias"] + + # Clear attentions as they have been attributed above. + if len(attentions) == 2: + attentions = [] + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = { + "old": f"output_blocks.{i}.1", + "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, + new_checkpoint, + unet_state_dict, + additional_replacements=[meta_path], + config=config, + ) + else: + resnet_0_paths = renew_resnet_paths( + output_block_layers, n_shave_prefix_segments=1 + ) + for path in resnet_0_paths: + old_path = ".".join(["output_blocks", str(i), path["old"]]) + new_path = ".".join( + [ + "up_blocks", + str(block_id), + "resnets", + str(layer_in_block_id), + path["new"], + ] + ) + + new_checkpoint[new_path] = unet_state_dict[old_path] + + if controlnet: + # conditioning embedding + + orig_index = 0 + + new_checkpoint[ + "controlnet_cond_embedding.conv_in.weight" + ] = unet_state_dict.pop(f"input_hint_block.{orig_index}.weight") + new_checkpoint["controlnet_cond_embedding.conv_in.bias"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.bias" + ) + + orig_index += 2 + + diffusers_index = 0 + + while diffusers_index < 6: + new_checkpoint[ + f"controlnet_cond_embedding.blocks.{diffusers_index}.weight" + ] = unet_state_dict.pop(f"input_hint_block.{orig_index}.weight") + new_checkpoint[ + f"controlnet_cond_embedding.blocks.{diffusers_index}.bias" + ] = unet_state_dict.pop(f"input_hint_block.{orig_index}.bias") + diffusers_index += 1 + orig_index += 2 + + new_checkpoint[ + "controlnet_cond_embedding.conv_out.weight" + ] = unet_state_dict.pop(f"input_hint_block.{orig_index}.weight") + new_checkpoint["controlnet_cond_embedding.conv_out.bias"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.bias" + ) + + # down blocks + for i in range(num_input_blocks): + new_checkpoint[f"controlnet_down_blocks.{i}.weight"] = unet_state_dict.pop( + f"zero_convs.{i}.0.weight" + ) + new_checkpoint[f"controlnet_down_blocks.{i}.bias"] = unet_state_dict.pop( + f"zero_convs.{i}.0.bias" + ) + + # mid block + new_checkpoint["controlnet_mid_block.weight"] = unet_state_dict.pop( + "middle_block_out.0.weight" + ) + new_checkpoint["controlnet_mid_block.bias"] = unet_state_dict.pop( + "middle_block_out.0.bias" + ) + + return new_checkpoint + + +def create_vae_diffusers_config(original_config, image_size: int): + """ + Creates a config for the diffusers based on the config of the LDM model. + """ + vae_params = original_config.model.params.first_stage_config.params.ddconfig + _ = original_config.model.params.first_stage_config.params.embed_dim + + block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult] + down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) + up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) + + config = { + "sample_size": image_size, + "in_channels": vae_params.in_channels, + "out_channels": vae_params.out_ch, + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "latent_channels": vae_params.z_channels, + "layers_per_block": vae_params.num_res_blocks, + } + return config + + +def convert_ldm_vae_checkpoint(checkpoint, config): + # extract state dict for VAE + vae_state_dict = {} + vae_key = "first_stage_model." + keys = list(checkpoint.keys()) + for key in keys: + if key.startswith(vae_key): + vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) + + new_checkpoint = {} + + new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] + new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] + new_checkpoint["encoder.conv_out.weight"] = vae_state_dict[ + "encoder.conv_out.weight" + ] + new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] + new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict[ + "encoder.norm_out.weight" + ] + new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict[ + "encoder.norm_out.bias" + ] + + new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] + new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] + new_checkpoint["decoder.conv_out.weight"] = vae_state_dict[ + "decoder.conv_out.weight" + ] + new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] + new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict[ + "decoder.norm_out.weight" + ] + new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict[ + "decoder.norm_out.bias" + ] + + new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] + new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] + new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] + new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] + + # Retrieves the keys for the encoder down blocks only + num_down_blocks = len( + { + ".".join(layer.split(".")[:3]) + for layer in vae_state_dict + if "encoder.down" in layer + } + ) + down_blocks = { + layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] + for layer_id in range(num_down_blocks) + } + + # Retrieves the keys for the decoder up blocks only + num_up_blocks = len( + { + ".".join(layer.split(".")[:3]) + for layer in vae_state_dict + if "decoder.up" in layer + } + ) + up_blocks = { + layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] + for layer_id in range(num_up_blocks) + } + + for i in range(num_down_blocks): + resnets = [ + key + for key in down_blocks[i] + if f"down.{i}" in key and f"down.{i}.downsample" not in key + ] + + if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: + new_checkpoint[ + f"encoder.down_blocks.{i}.downsamplers.0.conv.weight" + ] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.weight") + new_checkpoint[ + f"encoder.down_blocks.{i}.downsamplers.0.conv.bias" + ] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.bias") + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} + assign_to_checkpoint( + paths, + new_checkpoint, + vae_state_dict, + additional_replacements=[meta_path], + config=config, + ) + + mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint( + paths, + new_checkpoint, + vae_state_dict, + additional_replacements=[meta_path], + config=config, + ) + + mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint( + paths, + new_checkpoint, + vae_state_dict, + additional_replacements=[meta_path], + config=config, + ) + conv_attn_to_linear(new_checkpoint) + + for i in range(num_up_blocks): + block_id = num_up_blocks - 1 - i + resnets = [ + key + for key in up_blocks[block_id] + if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key + ] + + if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: + new_checkpoint[ + f"decoder.up_blocks.{i}.upsamplers.0.conv.weight" + ] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.weight"] + new_checkpoint[ + f"decoder.up_blocks.{i}.upsamplers.0.conv.bias" + ] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.bias"] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} + assign_to_checkpoint( + paths, + new_checkpoint, + vae_state_dict, + additional_replacements=[meta_path], + config=config, + ) + + mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint( + paths, + new_checkpoint, + vae_state_dict, + additional_replacements=[meta_path], + config=config, + ) + + mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint( + paths, + new_checkpoint, + vae_state_dict, + additional_replacements=[meta_path], + config=config, + ) + conv_attn_to_linear(new_checkpoint) + return new_checkpoint + + +def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("nin_shortcut", "conv_shortcut") + new_item = shave_segments( + new_item, n_shave_prefix_segments=n_shave_prefix_segments + ) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("norm.weight", "group_norm.weight") + new_item = new_item.replace("norm.bias", "group_norm.bias") + + new_item = new_item.replace("q.weight", "to_q.weight") + new_item = new_item.replace("q.bias", "to_q.bias") + + new_item = new_item.replace("k.weight", "to_k.weight") + new_item = new_item.replace("k.bias", "to_k.bias") + + new_item = new_item.replace("v.weight", "to_v.weight") + new_item = new_item.replace("v.bias", "to_v.bias") + + new_item = new_item.replace("proj_out.weight", "to_out.0.weight") + new_item = new_item.replace("proj_out.bias", "to_out.0.bias") + + new_item = shave_segments( + new_item, n_shave_prefix_segments=n_shave_prefix_segments + ) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def conv_attn_to_linear(checkpoint): + keys = list(checkpoint.keys()) + attn_keys = ["query.weight", "key.weight", "value.weight"] + for key in keys: + if ".".join(key.split(".")[-2:]) in attn_keys: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0, 0] + elif "proj_attn.weight" in key: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0] + + +def convert_from_original_zero123_ckpt( + checkpoint_path, original_config_file, extract_ema, device +): + ckpt = torch.load(checkpoint_path, map_location=device) + global_step = ckpt["global_step"] + checkpoint = ckpt["state_dict"] + del ckpt + torch.cuda.empty_cache() + + from omegaconf import OmegaConf + + original_config = OmegaConf.load(original_config_file) + model_type = original_config.model.params.cond_stage_config.target.split(".")[-1] + num_in_channels = 8 + original_config["model"]["params"]["unet_config"]["params"][ + "in_channels" + ] = num_in_channels + prediction_type = "epsilon" + image_size = 256 + num_train_timesteps = ( + getattr(original_config.model.params, "timesteps", None) or 1000 + ) + + beta_start = getattr(original_config.model.params, "linear_start", None) or 0.02 + beta_end = getattr(original_config.model.params, "linear_end", None) or 0.085 + scheduler = DDIMScheduler( + beta_end=beta_end, + beta_schedule="scaled_linear", + beta_start=beta_start, + num_train_timesteps=num_train_timesteps, + steps_offset=1, + clip_sample=False, + set_alpha_to_one=False, + prediction_type=prediction_type, + ) + scheduler.register_to_config(clip_sample=False) + + # Convert the UNet2DConditionModel model. + upcast_attention = None + unet_config = create_unet_diffusers_config(original_config, image_size=image_size) + unet_config["upcast_attention"] = upcast_attention + with init_empty_weights(): + unet = UNet2DConditionModel(**unet_config) + converted_unet_checkpoint = convert_ldm_unet_checkpoint( + checkpoint, unet_config, path=None, extract_ema=extract_ema + ) + for param_name, param in converted_unet_checkpoint.items(): + set_module_tensor_to_device(unet, param_name, "cpu", value=param) + + # Convert the VAE model. + vae_config = create_vae_diffusers_config(original_config, image_size=image_size) + converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config) + + if ( + "model" in original_config + and "params" in original_config.model + and "scale_factor" in original_config.model.params + ): + vae_scaling_factor = original_config.model.params.scale_factor + else: + vae_scaling_factor = 0.18215 # default SD scaling factor + + vae_config["scaling_factor"] = vae_scaling_factor + + with init_empty_weights(): + vae = AutoencoderKL(**vae_config) + + for param_name, param in converted_vae_checkpoint.items(): + set_module_tensor_to_device(vae, param_name, "cpu", value=param) + + feature_extractor = CLIPImageProcessor.from_pretrained( + "lambdalabs/sd-image-variations-diffusers", subfolder="feature_extractor" + ) + image_encoder = CLIPVisionModelWithProjection.from_pretrained( + "lambdalabs/sd-image-variations-diffusers", subfolder="image_encoder" + ) + + clip_camera_projection = CLIPCameraProjection(additional_embeddings=4) + clip_camera_projection.load_state_dict( + { + "proj.weight": checkpoint["cc_projection.weight"].cpu(), + "proj.bias": checkpoint["cc_projection.bias"].cpu(), + } + ) + + pipe = Zero123Pipeline( + vae, + image_encoder, + unet, + scheduler, + None, + feature_extractor, + clip_camera_projection, + requires_safety_checker=False, + ) + + return pipe + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--checkpoint_path", + default=None, + type=str, + required=True, + help="Path to the checkpoint to convert.", + ) + parser.add_argument( + "--original_config_file", + default=None, + type=str, + help="The YAML config file corresponding to the original architecture.", + ) + parser.add_argument( + "--extract_ema", + action="store_true", + help=( + "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" + " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" + " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." + ), + ) + parser.add_argument( + "--to_safetensors", + action="store_true", + help="Whether to store pipeline in safetensors format or not.", + ) + parser.add_argument( + "--half", action="store_true", help="Save weights in half precision." + ) + parser.add_argument( + "--dump_path", + default=None, + type=str, + required=True, + help="Path to the output model.", + ) + parser.add_argument( + "--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)" + ) + args = parser.parse_args() + + pipe = convert_from_original_zero123_ckpt( + checkpoint_path=args.checkpoint_path, + original_config_file=args.original_config_file, + extract_ema=args.extract_ema, + device=args.device, + ) + + if args.half: + pipe.to(torch_dtype=torch.float16) + + pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..6f31c1d --- /dev/null +++ b/setup.py @@ -0,0 +1,21 @@ +from setuptools import find_packages, setup + +setup( + name="threestudio", + version='"0.2.3"', # the current version of your package + packages=find_packages(), # automatically discover all packages and subpackages + url="https://github.com/threestudio-project/threestudio", # replace with the URL of your project + author="Yuan-Chen Guo and Ruizhi Shao and Ying-Tian Liu and Christian Laforte and Vikram Voleti and Guan Luo and Chia-Hao Chen and Zi-Xin Zou and Chen Wang and Yan-Pei Cao and Song-Hai Zhang", # replace with your name + author_email="shaorz20@mails.tsinghua.edu.cn", # replace with your email + description="threestudio is a unified framework for 3D content creation from text prompts, single images, and few-shot images, by lifting 2D text-to-image generation models.", # replace with a brief description of your project + install_requires=[ + # list of packages your project depends on + # you can specify versions as well, e.g. 'numpy>=1.15.1' + ], + classifiers=[ + # classifiers help users find your project by categorizing it + # for a list of valid classifiers, see https://pypi.org/classifiers/ + "License :: Apache-2.0", + "Programming Language :: Python :: 3", + ], +) diff --git a/threestudio.ipynb b/threestudio.ipynb new file mode 100644 index 0000000..d61d8e3 --- /dev/null +++ b/threestudio.ipynb @@ -0,0 +1,303 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YadOCCCyXT0y" + }, + "outputs": [], + "source": [ + "!nvidia-smi" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "S7vZFQkeq_Vk" + }, + "source": [ + "Clone threestudio repo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ubuVj4z0MhHh" + }, + "outputs": [], + "source": [ + "!git clone https://github.com/threestudio-project/threestudio.git" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HrGD3RtXXsB9" + }, + "outputs": [], + "source": [ + "%cd threestudio" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "aXqmxXX0Jb6m" + }, + "source": [ + "Install dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HKZQNbNmX20t" + }, + "outputs": [], + "source": [ + "!pip install ninja\n", + "!pip install lightning==2.0.0 omegaconf==2.3.0 jaxtyping typeguard diffusers transformers accelerate opencv-python tensorboard matplotlib imageio imageio[ffmpeg] trimesh bitsandbytes sentencepiece safetensors huggingface_hub libigl xatlas networkx pysdf PyMCubes wandb torchmetrics controlnet_aux\n", + "!pip install einops kornia taming-transformers-rom1504 git+https://github.com/openai/CLIP.git # zero123\n", + "!pip install open3d plotly # mesh visualization" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "b8U3rne0JTgs" + }, + "source": [ + "And build some dependencies manually. This may take a while." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BFHI67e0_65a" + }, + "outputs": [], + "source": [ + "!pip install git+https://github.com/ashawkey/envlight.git\n", + "!pip install git+https://github.com/KAIR-BAIR/nerfacc.git@v0.5.2\n", + "!pip install git+https://github.com/NVlabs/nvdiffrast.git\n", + "!pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "QxY_7clwrJ18" + }, + "source": [ + "Login to HuggingFace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "T4eeJFCZAZUV" + }, + "outputs": [], + "source": [ + "from huggingface_hub import interpreter_login\n", + "\n", + "interpreter_login()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "Eeqa6u5QrPTj" + }, + "source": [ + "Now create your own 3D model from text prompts\n", + "\n", + "Here we use the DreamFusion model with DeepFloyd-IF guidance. You may try other models by using different running commands given [here](https://github.com/threestudio-project/threestudio#supported-models)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pZUD5VcS88yz" + }, + "outputs": [], + "source": [ + "prompt = \"a zoomed out DSLR photo of a baby bunny sitting on top of a stack of pancakes\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Uy61bM84K7Qi" + }, + "outputs": [], + "source": [ + "!python launch.py --config configs/dreamfusion-if.yaml --train --gpu 0 system.prompt_processor.prompt=\"$prompt\" trainer.max_steps=10000 system.prompt_processor.spawn=false" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "UqrrK9FxrYgd" + }, + "source": [ + "Display the rendered video" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "J14FrBwMk5m9" + }, + "outputs": [], + "source": [ + "from IPython.display import HTML\n", + "from base64 import b64encode\n", + "def display_video(video_path):\n", + " mp4 = open(video_path,'rb').read()\n", + " data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", + " return HTML(\"\"\"\n", + " \n", + " \"\"\" % data_url)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "aaQCVmiCp87c" + }, + "outputs": [], + "source": [ + "# you will see the path to the saving directory at the end of the training logs\n", + "# replace save_dir below with that path\n", + "save_dir = 'path/to/save/dir'\n", + "\n", + "import os\n", + "import glob\n", + "video_path = glob.glob(os.path.join(save_dir, \"*-test.mp4\"))[0]\n", + "display_video(video_path)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "UlUL4ZPn2xQV" + }, + "source": [ + "Extract the object mesh.\n", + "\n", + "Here we use an empirical threshold value. You can also first try `system.geometry.isosurface_threshold=auto` and visualize it. Then you can manually adjust the threshold according to the automatically determined value shown in the logs. Increase it if there are too many floaters and decrease it if the geometry is incomplete. \n", + "\n", + "\n", + "The extraction process takes around 2 mins on T4." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "f-R26yEXqgyL" + }, + "outputs": [], + "source": [ + "!python launch.py --config $save_dir/../configs/parsed.yaml --export --gpu 0 resume=$save_dir/../ckpts/last.ckpt system.exporter_type=mesh-exporter system.exporter.context_type=cuda system.geometry.isosurface_threshold=15.0 " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "iocD0gl870vq" + }, + "source": [ + "Visualize the mesh. Or you can directly download the export assets and use them locally." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "71-J9PRIyp99" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import open3d as o3d\n", + "import plotly.graph_objects as go\n", + "import glob\n", + "\n", + "mesh_path = glob.glob(os.path.join(save_dir, \"*-export/model.obj\"))[0]\n", + "mesh = o3d.io.read_triangle_mesh(mesh_path)\n", + "if not mesh.has_vertex_normals():\n", + " mesh.compute_vertex_normals()\n", + "if not mesh.has_triangle_normals():\n", + " mesh.compute_triangle_normals()\n", + "\n", + "triangles = np.asarray(mesh.triangles)\n", + "vertices = np.asarray(mesh.vertices)\n", + "colors = None\n", + "if mesh.has_triangle_normals():\n", + " colors = (0.5, 0.5, 0.5) + np.asarray(mesh.triangle_normals) * 0.5\n", + " colors = tuple(map(tuple, colors))\n", + "else:\n", + " colors = (1.0, 0.0, 0.0)\n", + "fig = go.Figure(\n", + " data=[\n", + " go.Mesh3d(\n", + " x=vertices[:,0],\n", + " y=vertices[:,1],\n", + " z=vertices[:,2],\n", + " i=triangles[:,0],\n", + " j=triangles[:,1],\n", + " k=triangles[:,2],\n", + " facecolor=colors,\n", + " opacity=0.50)\n", + " ],\n", + " layout=dict(\n", + " scene=dict(\n", + " xaxis=dict(visible=False),\n", + " yaxis=dict(visible=False),\n", + " zaxis=dict(visible=False)\n", + " )\n", + " )\n", + ")\n", + "fig.show()\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/threestudio/__init__.py b/threestudio/__init__.py new file mode 100644 index 0000000..9b7b83b --- /dev/null +++ b/threestudio/__init__.py @@ -0,0 +1,55 @@ +__modules__ = {} +__version__ = "0.2.3" + + +def register(name): + def decorator(cls): + if name in __modules__: + raise ValueError( + f"Module {name} already exists! Names of extensions conflict!" + ) + else: + __modules__[name] = cls + return cls + + return decorator + + +def find(name): + if ":" in name: + main_name, sub_name = name.split(":") + if "," in sub_name: + name_list = sub_name.split(",") + else: + name_list = [sub_name] + name_list.append(main_name) + NewClass = type( + f"{main_name}.{sub_name}", + tuple([__modules__[name] for name in name_list]), + {}, + ) + return NewClass + return __modules__[name] + + +### grammar sugar for logging utilities ### +import logging + +logger = logging.getLogger("pytorch_lightning") + +from pytorch_lightning.utilities.rank_zero import ( + rank_zero_debug, + rank_zero_info, + rank_zero_only, +) + +debug = rank_zero_debug +info = rank_zero_info + + +@rank_zero_only +def warn(*args, **kwargs): + logger.warn(*args, **kwargs) + + +from . import data, models, systems diff --git a/threestudio/data/__init__.py b/threestudio/data/__init__.py new file mode 100644 index 0000000..70aaeeb --- /dev/null +++ b/threestudio/data/__init__.py @@ -0,0 +1 @@ +from . import co3d, image, multiview, uncond, uncond_eff diff --git a/threestudio/data/co3d.py b/threestudio/data/co3d.py new file mode 100644 index 0000000..4916888 --- /dev/null +++ b/threestudio/data/co3d.py @@ -0,0 +1,713 @@ +import gzip +import json +import os +import warnings +from dataclasses import dataclass, field +from typing import List + +import cv2 +import numpy as np +import pytorch_lightning as pl +import torch +import torchvision.transforms.functional as TF +from PIL import Image +from torch.utils.data import DataLoader, Dataset, IterableDataset + +from threestudio import register +from threestudio.data.uncond import ( + RandomCameraDataModuleConfig, + RandomCameraDataset, + RandomCameraIterableDataset, +) +from threestudio.utils.config import parse_structured +from threestudio.utils.misc import get_rank +from threestudio.utils.ops import ( + get_mvp_matrix, + get_projection_matrix, + get_ray_directions, + get_rays, +) +from threestudio.utils.typing import * + + +def _load_16big_png_depth(depth_png) -> np.ndarray: + with Image.open(depth_png) as depth_pil: + # the image is stored with 16-bit depth but PIL reads it as I (32 bit). + # we cast it to uint16, then reinterpret as float16, then cast to float32 + depth = ( + np.frombuffer(np.array(depth_pil, dtype=np.uint16), dtype=np.float16) + .astype(np.float32) + .reshape((depth_pil.size[1], depth_pil.size[0])) + ) + return depth + + +def _load_depth(path, scale_adjustment) -> np.ndarray: + if not path.lower().endswith(".png"): + raise ValueError('unsupported depth file name "%s"' % path) + + d = _load_16big_png_depth(path) * scale_adjustment + d[~np.isfinite(d)] = 0.0 + return d[None] # fake feature channel + + +# Code adapted from https://github.com/eldar/snes/blob/473ff2b1f6/3rdparty/co3d/dataset/co3d_dataset.py +def _get_1d_bounds(arr): + nz = np.flatnonzero(arr) + return nz[0], nz[-1] + + +def get_bbox_from_mask(mask, thr, decrease_quant=0.05): + # bbox in xywh + masks_for_box = np.zeros_like(mask) + while masks_for_box.sum() <= 1.0: + masks_for_box = (mask > thr).astype(np.float32) + thr -= decrease_quant + if thr <= 0.0: + warnings.warn(f"Empty masks_for_bbox (thr={thr}) => using full image.") + + x0, x1 = _get_1d_bounds(masks_for_box.sum(axis=-2)) + y0, y1 = _get_1d_bounds(masks_for_box.sum(axis=-1)) + + return x0, y0, x1 - x0, y1 - y0 + + +def get_clamp_bbox(bbox, box_crop_context=0.0, impath=""): + # box_crop_context: rate of expansion for bbox + # returns possibly expanded bbox xyxy as float + + # increase box size + if box_crop_context > 0.0: + c = box_crop_context + bbox = bbox.astype(np.float32) + bbox[0] -= bbox[2] * c / 2 + bbox[1] -= bbox[3] * c / 2 + bbox[2] += bbox[2] * c + bbox[3] += bbox[3] * c + + if (bbox[2:] <= 1.0).any(): + warnings.warn(f"squashed image {impath}!!") + return None + + # bbox[2:] = np.clip(bbox[2:], 2, ) + bbox[2:] = np.maximum(bbox[2:], 2) + bbox[2:] += bbox[0:2] + 1 # convert to [xmin, ymin, xmax, ymax] + # +1 because upper bound is not inclusive + + return bbox + + +def crop_around_box(tensor, bbox, impath=""): + bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0.0, tensor.shape[-2]) + bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0.0, tensor.shape[-3]) + bbox = bbox.round().astype(np.longlong) + return tensor[bbox[1] : bbox[3], bbox[0] : bbox[2], ...] + + +def resize_image(image, height, width, mode="bilinear"): + if image.shape[:2] == (height, width): + return image, 1.0, np.ones_like(image[..., :1]) + + image = torch.from_numpy(image).permute(2, 0, 1) + minscale = min(height / image.shape[-2], width / image.shape[-1]) + imre = torch.nn.functional.interpolate( + image[None], + scale_factor=minscale, + mode=mode, + align_corners=False if mode == "bilinear" else None, + recompute_scale_factor=True, + )[0] + + # pyre-fixme[19]: Expected 1 positional argument. + imre_ = torch.zeros(image.shape[0], height, width) + imre_[:, 0 : imre.shape[1], 0 : imre.shape[2]] = imre + # pyre-fixme[6]: For 2nd param expected `int` but got `Optional[int]`. + # pyre-fixme[6]: For 3rd param expected `int` but got `Optional[int]`. + mask = torch.zeros(1, height, width) + mask[:, 0 : imre.shape[1], 0 : imre.shape[2]] = 1.0 + return imre_.permute(1, 2, 0).numpy(), minscale, mask.permute(1, 2, 0).numpy() + + +# Code adapted from https://github.com/POSTECH-CVLab/PeRFception/data_util/co3d.py +def similarity_from_cameras(c2w, fix_rot=False, radius=1.0): + """ + Get a similarity transform to normalize dataset + from c2w (OpenCV convention) cameras + :param c2w: (N, 4) + :return T (4,4) , scale (float) + """ + t = c2w[:, :3, 3] + R = c2w[:, :3, :3] + + # (1) Rotate the world so that z+ is the up axis + # we estimate the up axis by averaging the camera up axes + ups = np.sum(R * np.array([0, -1.0, 0]), axis=-1) + world_up = np.mean(ups, axis=0) + world_up /= np.linalg.norm(world_up) + + up_camspace = np.array([0.0, 0.0, 1.0]) + c = (up_camspace * world_up).sum() + cross = np.cross(world_up, up_camspace) + skew = np.array( + [ + [0.0, -cross[2], cross[1]], + [cross[2], 0.0, -cross[0]], + [-cross[1], cross[0], 0.0], + ] + ) + if c > -1: + R_align = np.eye(3) + skew + (skew @ skew) * 1 / (1 + c) + else: + # In the unlikely case the original data has y+ up axis, + # rotate 180-deg about x axis + R_align = np.array([[-1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]) + + if fix_rot: + R_align = np.eye(3) + R = np.eye(3) + else: + R = R_align @ R + fwds = np.sum(R * np.array([0, 0.0, 1.0]), axis=-1) + t = (R_align @ t[..., None])[..., 0] + + # (2) Recenter the scene using camera center rays + # find the closest point to the origin for each camera's center ray + nearest = t + (fwds * -t).sum(-1)[:, None] * fwds + + # median for more robustness + translate = -np.median(nearest, axis=0) + + # translate = -np.mean(t, axis=0) # DEBUG + + transform = np.eye(4) + transform[:3, 3] = translate + transform[:3, :3] = R_align + + # (3) Rescale the scene using camera distances + scale = radius / np.median(np.linalg.norm(t + translate, axis=-1)) + + return transform, scale + + +@dataclass +class Co3dDataModuleConfig: + root_dir: str = "" + batch_size: int = 1 + height: int = 256 + width: int = 256 + load_preprocessed: bool = False + cam_scale_factor: float = 0.95 + max_num_frames: int = 300 + v2_mode: bool = True + use_mask: bool = True + box_crop: bool = True + box_crop_mask_thr: float = 0.4 + box_crop_context: float = 0.3 + train_num_rays: int = -1 + train_views: Optional[list] = None + train_split: str = "train" + val_split: str = "val" + test_split: str = "test" + scale_radius: float = 1.0 + use_random_camera: bool = True + random_camera: dict = field(default_factory=dict) + rays_noise_scale: float = 0.0 + render_path: str = "circle" + + +class Co3dDatasetBase: + def setup(self, cfg, split): + self.split = split + self.rank = get_rank() + self.cfg: Co3dDataModuleConfig = cfg + + if self.cfg.use_random_camera: + random_camera_cfg = parse_structured( + RandomCameraDataModuleConfig, self.cfg.get("random_camera", {}) + ) + if split == "train": + self.random_pose_generator = RandomCameraIterableDataset( + random_camera_cfg + ) + else: + self.random_pose_generator = RandomCameraDataset( + random_camera_cfg, split + ) + + self.use_mask = self.cfg.use_mask + cam_scale_factor = self.cfg.cam_scale_factor + + assert os.path.exists(self.cfg.root_dir), f"{self.cfg.root_dir} doesn't exist!" + + cam_trans = np.diag(np.array([-1, -1, 1, 1], dtype=np.float32)) + scene_number = self.cfg.root_dir.split("/")[-1] + json_path = os.path.join(self.cfg.root_dir, "..", "frame_annotations.jgz") + with gzip.open(json_path, "r") as fp: + all_frames_data = json.load(fp) + + frame_data, images, intrinsics, extrinsics, image_sizes = [], [], [], [], [] + masks = [] + depths = [] + + for temporal_data in all_frames_data: + if temporal_data["sequence_name"] == scene_number: + frame_data.append(temporal_data) + + self.all_directions = [] + self.all_fg_masks = [] + for frame in frame_data: + if "unseen" in frame["meta"]["frame_type"]: + continue + img = cv2.imread( + os.path.join(self.cfg.root_dir, "..", "..", frame["image"]["path"]) + ) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0 + + # TODO: use estimated depth + depth = _load_depth( + os.path.join(self.cfg.root_dir, "..", "..", frame["depth"]["path"]), + frame["depth"]["scale_adjustment"], + )[0] + + H, W = frame["image"]["size"] + image_size = np.array([H, W]) + fxy = np.array(frame["viewpoint"]["focal_length"]) + cxy = np.array(frame["viewpoint"]["principal_point"]) + R = np.array(frame["viewpoint"]["R"]) + T = np.array(frame["viewpoint"]["T"]) + + if self.cfg.v2_mode: + min_HW = min(W, H) + image_size_half = np.array([W * 0.5, H * 0.5], dtype=np.float32) + scale_arr = np.array([min_HW * 0.5, min_HW * 0.5], dtype=np.float32) + fxy_x = fxy * scale_arr + prp_x = np.array([W * 0.5, H * 0.5], dtype=np.float32) - cxy * scale_arr + cxy = (image_size_half - prp_x) / image_size_half + fxy = fxy_x / image_size_half + + scale_arr = np.array([W * 0.5, H * 0.5], dtype=np.float32) + focal = fxy * scale_arr + prp = -1.0 * (cxy - 1.0) * scale_arr + + pose = np.eye(4) + pose[:3, :3] = R + pose[:3, 3:] = -R @ T[..., None] + # original camera: x left, y up, z in (Pytorch3D) + # transformed camera: x right, y down, z in (OpenCV) + pose = pose @ cam_trans + intrinsic = np.array( + [ + [focal[0], 0.0, prp[0], 0.0], + [0.0, focal[1], prp[1], 0.0], + [0.0, 0.0, 1.0, 0.0], + [0.0, 0.0, 0.0, 1.0], + ] + ) + + if any([np.all(pose == _pose) for _pose in extrinsics]): + continue + + image_sizes.append(image_size) + intrinsics.append(intrinsic) + extrinsics.append(pose) + images.append(img) + depths.append(depth) + self.all_directions.append(get_ray_directions(W, H, focal, prp)) + + # vis_utils.vis_depth_pcd([depth], [pose], intrinsic, [(img * 255).astype(np.uint8)]) + + if self.use_mask: + mask = np.array( + Image.open( + os.path.join( + self.cfg.root_dir, "..", "..", frame["mask"]["path"] + ) + ) + ) + mask = mask.astype(np.float32) / 255.0 # (h, w) + else: + mask = torch.ones_like(img[..., 0]) + self.all_fg_masks.append(mask) + + intrinsics = np.stack(intrinsics) + extrinsics = np.stack(extrinsics) + image_sizes = np.stack(image_sizes) + self.all_directions = torch.stack(self.all_directions, dim=0) + self.all_fg_masks = np.stack(self.all_fg_masks, 0) + + H_median, W_median = np.median( + np.stack([image_size for image_size in image_sizes]), axis=0 + ) + + H_inlier = np.abs(image_sizes[:, 0] - H_median) / H_median < 0.1 + W_inlier = np.abs(image_sizes[:, 1] - W_median) / W_median < 0.1 + inlier = np.logical_and(H_inlier, W_inlier) + dists = np.linalg.norm( + extrinsics[:, :3, 3] - np.median(extrinsics[:, :3, 3], axis=0), axis=-1 + ) + med = np.median(dists) + good_mask = dists < (med * 5.0) + inlier = np.logical_and(inlier, good_mask) + + if inlier.sum() != 0: + intrinsics = intrinsics[inlier] + extrinsics = extrinsics[inlier] + image_sizes = image_sizes[inlier] + images = [images[i] for i in range(len(inlier)) if inlier[i]] + depths = [depths[i] for i in range(len(inlier)) if inlier[i]] + self.all_directions = self.all_directions[inlier] + self.all_fg_masks = self.all_fg_masks[inlier] + + extrinsics = np.stack(extrinsics) + T, sscale = similarity_from_cameras(extrinsics, radius=self.cfg.scale_radius) + extrinsics = T @ extrinsics + + extrinsics[:, :3, 3] *= sscale * cam_scale_factor + + depths = [depth * sscale * cam_scale_factor for depth in depths] + + num_frames = len(extrinsics) + + if self.cfg.max_num_frames < num_frames: + num_frames = self.cfg.max_num_frames + extrinsics = extrinsics[:num_frames] + intrinsics = intrinsics[:num_frames] + image_sizes = image_sizes[:num_frames] + images = images[:num_frames] + depths = depths[:num_frames] + self.all_directions = self.all_directions[:num_frames] + self.all_fg_masks = self.all_fg_masks[:num_frames] + + if self.cfg.box_crop: + print("cropping...") + crop_masks = [] + crop_imgs = [] + crop_depths = [] + crop_directions = [] + crop_xywhs = [] + max_sl = 0 + for i in range(num_frames): + bbox_xywh = np.array( + get_bbox_from_mask(self.all_fg_masks[i], self.cfg.box_crop_mask_thr) + ) + clamp_bbox_xywh = get_clamp_bbox(bbox_xywh, self.cfg.box_crop_context) + max_sl = max(clamp_bbox_xywh[2] - clamp_bbox_xywh[0], max_sl) + max_sl = max(clamp_bbox_xywh[3] - clamp_bbox_xywh[1], max_sl) + mask = crop_around_box(self.all_fg_masks[i][..., None], clamp_bbox_xywh) + img = crop_around_box(images[i], clamp_bbox_xywh) + depth = crop_around_box(depths[i][..., None], clamp_bbox_xywh) + + # resize to the same shape + mask, _, _ = resize_image(mask, self.cfg.height, self.cfg.width) + depth, _, _ = resize_image(depth, self.cfg.height, self.cfg.width) + img, scale, _ = resize_image(img, self.cfg.height, self.cfg.width) + fx, fy, cx, cy = ( + intrinsics[i][0, 0], + intrinsics[i][1, 1], + intrinsics[i][0, 2], + intrinsics[i][1, 2], + ) + + crop_masks.append(mask) + crop_imgs.append(img) + crop_depths.append(depth) + crop_xywhs.append(clamp_bbox_xywh) + crop_directions.append( + get_ray_directions( + self.cfg.height, + self.cfg.width, + (fx * scale, fy * scale), + ( + (cx - clamp_bbox_xywh[0]) * scale, + (cy - clamp_bbox_xywh[1]) * scale, + ), + ) + ) + + # # pad all images to the same shape + # for i in range(num_frames): + # uh = (max_sl - crop_imgs[i].shape[0]) // 2 # h + # dh = max_sl - crop_imgs[i].shape[0] - uh + # lw = (max_sl - crop_imgs[i].shape[1]) // 2 + # rw = max_sl - crop_imgs[i].shape[1] - lw + # crop_masks[i] = np.pad(crop_masks[i], pad_width=((uh, dh), (lw, rw), (0, 0)), mode='constant', constant_values=0.) + # crop_imgs[i] = np.pad(crop_imgs[i], pad_width=((uh, dh), (lw, rw), (0, 0)), mode='constant', constant_values=1.) + # crop_depths[i] = np.pad(crop_depths[i], pad_width=((uh, dh), (lw, rw), (0, 0)), mode='constant', constant_values=0.) + # fx, fy, cx, cy = intrinsics[i][0, 0], intrinsics[i][1, 1], intrinsics[i][0, 2], intrinsics[i][1, 2] + # crop_directions.append(get_ray_directions(max_sl, max_sl, (fx, fy), (cx - crop_xywhs[i][0] + lw, cy - crop_xywhs[i][1] + uh))) + # self.w, self.h = max_sl, max_sl + + images = crop_imgs + depths = crop_depths + self.all_fg_masks = np.stack(crop_masks, 0) + self.all_directions = torch.from_numpy(np.stack(crop_directions, 0)) + + # self.width, self.height = self.w, self.h + + self.all_c2w = torch.from_numpy( + ( + extrinsics + @ np.diag(np.array([1, -1, -1, 1], dtype=np.float32))[None, ...] + )[..., :3, :4] + ) + self.all_images = torch.from_numpy(np.stack(images, axis=0)) + self.all_depths = torch.from_numpy(np.stack(depths, axis=0)) + + # self.all_c2w = [] + # self.all_images = [] + # for i in range(num_frames): + # # convert to: x right, y up, z back (OpenGL) + # c2w = torch.from_numpy(extrinsics[i] @ np.diag(np.array([1, -1, -1, 1], dtype=np.float32)))[:3, :4] + # self.all_c2w.append(c2w) + # img = torch.from_numpy(images[i]) + # self.all_images.append(img) + + # TODO: save data for fast loading next time + if self.cfg.load_preprocessed and os.path.exists( + self.cfg.root_dir, "nerf_preprocessed.npy" + ): + pass + + i_all = np.arange(num_frames) + + if self.cfg.train_views is None: + i_test = i_all[::10] + i_val = i_test + i_train = np.array([i for i in i_all if not i in i_test]) + else: + # use provided views + i_train = self.cfg.train_views + i_test = np.array([i for i in i_all if not i in i_train]) + i_val = i_test + + if self.split == "train": + print("[INFO] num of train views: ", len(i_train)) + print("[INFO] train view ids = ", i_train) + + i_split = {"train": i_train, "val": i_val, "test": i_all} + + # if self.split == 'test': + # self.all_c2w = create_spheric_poses(self.all_c2w[:,:,3], n_steps=self.cfg.n_test_traj_steps) + # self.all_images = torch.zeros((self.cfg.n_test_traj_steps, self.h, self.w, 3), dtype=torch.float32) + # self.all_fg_masks = torch.zeros((self.cfg.n_test_traj_steps, self.h, self.w), dtype=torch.float32) + # self.directions = self.directions[0].to(self.rank) + # else: + self.all_images, self.all_c2w = ( + self.all_images[i_split[self.split]], + self.all_c2w[i_split[self.split]], + ) + self.all_directions = self.all_directions[i_split[self.split]].to(self.rank) + self.all_fg_masks = torch.from_numpy(self.all_fg_masks)[i_split[self.split]] + self.all_depths = self.all_depths[i_split[self.split]] + # if render_random_pose: + # render_poses = random_pose(extrinsics[i_all], 50) + # elif render_scene_interp: + # render_poses = pose_interp(extrinsics[i_all], interp_fac) + # render_poses = spherical_poses(sscale * cam_scale_factor * np.eye(4)) + + # near, far = 0., 1. + # ndc_coeffs = (-1., -1.) + + self.all_c2w, self.all_images, self.all_fg_masks = ( + self.all_c2w.float().to(self.rank), + self.all_images.float().to(self.rank), + self.all_fg_masks.float().to(self.rank), + ) + + # self.all_c2w, self.all_images, self.all_fg_masks = \ + # self.all_c2w.float(), \ + # self.all_images.float(), \ + # self.all_fg_masks.float() + + self.all_depths = self.all_depths.float().to(self.rank) + + def get_all_images(self): + return self.all_images + + +class Co3dDataset(Dataset, Co3dDatasetBase): + def __init__(self, cfg, split): + self.setup(cfg, split) + + def __len__(self): + if self.split == "test": + if self.cfg.render_path == "circle": + return len(self.random_pose_generator) + else: + return len(self.all_images) + else: + return len(self.random_pose_generator) + # return len(self.all_images) + + def prepare_data(self, index): + # prepare batch data here + c2w = self.all_c2w[index] + light_positions = c2w[..., :3, -1] + directions = self.all_directions[index] + rays_o, rays_d = get_rays( + directions, c2w, keepdim=True, noise_scale=self.cfg.rays_noise_scale + ) + rgb = self.all_images[index] + depth = self.all_depths[index] + mask = self.all_fg_masks[index] + + # TODO: get projection matrix and mvp matrix + # proj_mtx = get_projection_matrix() + + batch = { + "rays_o": rays_o, + "rays_d": rays_d, + "mvp_mtx": 0, + "camera_positions": c2w[..., :3, -1], + "light_positions": light_positions, + "elevation": 0, + "azimuth": 0, + "camera_distances": 0, + "rgb": rgb, + "depth": depth, + "mask": mask, + } + + # c2w = self.all_c2w[index] + # return { + # 'index': index, + # 'c2w': c2w, + # 'light_positions': c2w[:3, -1], + # 'H': self.h, + # 'W': self.w + # } + + return batch + + def __getitem__(self, index): + if self.split == "test": + if self.cfg.render_path == "circle": + return self.random_pose_generator[index] + else: + return self.prepare_data(index) + else: + return self.random_pose_generator[index] + + +class Co3dIterableDataset(IterableDataset, Co3dDatasetBase): + def __init__(self, cfg, split): + self.setup(cfg, split) + self.idx = 0 + self.image_perm = torch.randperm(len(self.all_images)) + + def __iter__(self): + while True: + yield {} + + def collate(self, batch) -> Dict[str, Any]: + idx = self.image_perm[self.idx] + # prepare batch data here + c2w = self.all_c2w[idx][None] + light_positions = c2w[..., :3, -1] + directions = self.all_directions[idx][None] + rays_o, rays_d = get_rays( + directions, c2w, keepdim=True, noise_scale=self.cfg.rays_noise_scale + ) + rgb = self.all_images[idx][None] + depth = self.all_depths[idx][None] + mask = self.all_fg_masks[idx][None] + + if ( + self.cfg.train_num_rays != -1 + and self.cfg.train_num_rays < self.cfg.height * self.cfg.width + ): + _, height, width, _ = rays_o.shape + x = torch.randint( + 0, width, size=(self.cfg.train_num_rays,), device=rays_o.device + ) + y = torch.randint( + 0, height, size=(self.cfg.train_num_rays,), device=rays_o.device + ) + + rays_o = rays_o[:, y, x].unsqueeze(-2) + rays_d = rays_d[:, y, x].unsqueeze(-2) + directions = directions[:, y, x].unsqueeze(-2) + rgb = rgb[:, y, x].unsqueeze(-2) + mask = mask[:, y, x].unsqueeze(-2) + depth = depth[:, y, x].unsqueeze(-2) + + # TODO: get projection matrix and mvp matrix + # proj_mtx = get_projection_matrix() + + batch = { + "rays_o": rays_o, + "rays_d": rays_d, + "mvp_mtx": None, + "camera_positions": c2w[..., :3, -1], + "light_positions": light_positions, + "elevation": None, + "azimuth": None, + "camera_distances": None, + "rgb": rgb, + "depth": depth, + "mask": mask, + } + + if self.cfg.use_random_camera: + batch["random_camera"] = self.random_pose_generator.collate(None) + + # prepare batch data in system + # c2w = self.all_c2w[idx][None] + + # batch = { + # 'index': torch.tensor([idx]), + # 'c2w': c2w, + # 'light_positions': c2w[..., :3, -1], + # 'H': self.h, + # 'W': self.w + # } + + self.idx += 1 + if self.idx == len(self.all_images): + self.idx = 0 + self.image_perm = torch.randperm(len(self.all_images)) + # self.idx = (self.idx + 1) % len(self.all_images) + + return batch + + +@register("co3d-datamodule") +class Co3dDataModule(pl.LightningDataModule): + def __init__(self, cfg: Optional[Union[dict, DictConfig]] = None) -> None: + super().__init__() + self.cfg = parse_structured(Co3dDataModuleConfig, cfg) + + def setup(self, stage=None): + if stage in [None, "fit"]: + self.train_dataset = Co3dIterableDataset(self.cfg, self.cfg.train_split) + if stage in [None, "fit", "validate"]: + self.val_dataset = Co3dDataset(self.cfg, self.cfg.val_split) + if stage in [None, "test", "predict"]: + self.test_dataset = Co3dDataset(self.cfg, self.cfg.test_split) + + def prepare_data(self): + pass + + def general_loader(self, dataset, batch_size, collate_fn=None) -> DataLoader: + sampler = None + return DataLoader( + dataset, + num_workers=0, + batch_size=batch_size, + # pin_memory=True, + collate_fn=collate_fn, + ) + + def train_dataloader(self): + return self.general_loader( + self.train_dataset, batch_size=1, collate_fn=self.train_dataset.collate + ) + + def val_dataloader(self): + return self.general_loader(self.val_dataset, batch_size=1) + + def test_dataloader(self): + return self.general_loader(self.test_dataset, batch_size=1) + + def predict_dataloader(self): + return self.general_loader(self.test_dataset, batch_size=1) diff --git a/threestudio/data/image.py b/threestudio/data/image.py new file mode 100644 index 0000000..033c528 --- /dev/null +++ b/threestudio/data/image.py @@ -0,0 +1,350 @@ +import bisect +import math +import os +from dataclasses import dataclass, field + +import cv2 +import numpy as np +import pytorch_lightning as pl +import torch +import torch.nn.functional as F +from torch.utils.data import DataLoader, Dataset, IterableDataset + +import threestudio +from threestudio import register +from threestudio.data.uncond import ( + RandomCameraDataModuleConfig, + RandomCameraDataset, + RandomCameraIterableDataset, +) +from threestudio.utils.base import Updateable +from threestudio.utils.config import parse_structured +from threestudio.utils.misc import get_rank +from threestudio.utils.ops import ( + get_mvp_matrix, + get_projection_matrix, + get_ray_directions, + get_rays, +) +from threestudio.utils.typing import * + + +@dataclass +class SingleImageDataModuleConfig: + # height and width should be Union[int, List[int]] + # but OmegaConf does not support Union of containers + height: Any = 96 + width: Any = 96 + resolution_milestones: List[int] = field(default_factory=lambda: []) + default_elevation_deg: float = 0.0 + default_azimuth_deg: float = -180.0 + default_camera_distance: float = 1.2 + default_fovy_deg: float = 60.0 + image_path: str = "" + use_random_camera: bool = True + random_camera: dict = field(default_factory=dict) + rays_noise_scale: float = 2e-3 + batch_size: int = 1 + requires_depth: bool = False + requires_normal: bool = False + + rays_d_normalize: bool = True + + +class SingleImageDataBase: + def setup(self, cfg, split): + self.split = split + self.rank = get_rank() + self.cfg: SingleImageDataModuleConfig = cfg + + if self.cfg.use_random_camera: + random_camera_cfg = parse_structured( + RandomCameraDataModuleConfig, self.cfg.get("random_camera", {}) + ) + if split == "train": + self.random_pose_generator = RandomCameraIterableDataset( + random_camera_cfg + ) + else: + self.random_pose_generator = RandomCameraDataset( + random_camera_cfg, split + ) + + elevation_deg = torch.FloatTensor([self.cfg.default_elevation_deg]) + azimuth_deg = torch.FloatTensor([self.cfg.default_azimuth_deg]) + camera_distance = torch.FloatTensor([self.cfg.default_camera_distance]) + + elevation = elevation_deg * math.pi / 180 + azimuth = azimuth_deg * math.pi / 180 + camera_position: Float[Tensor, "1 3"] = torch.stack( + [ + camera_distance * torch.cos(elevation) * torch.cos(azimuth), + camera_distance * torch.cos(elevation) * torch.sin(azimuth), + camera_distance * torch.sin(elevation), + ], + dim=-1, + ) + + center: Float[Tensor, "1 3"] = torch.zeros_like(camera_position) + up: Float[Tensor, "1 3"] = torch.as_tensor([0, 0, 1], dtype=torch.float32)[None] + + light_position: Float[Tensor, "1 3"] = camera_position + lookat: Float[Tensor, "1 3"] = F.normalize(center - camera_position, dim=-1) + right: Float[Tensor, "1 3"] = F.normalize(torch.cross(lookat, up), dim=-1) + up = F.normalize(torch.cross(right, lookat), dim=-1) + self.c2w: Float[Tensor, "1 3 4"] = torch.cat( + [torch.stack([right, up, -lookat], dim=-1), camera_position[:, :, None]], + dim=-1, + ) + self.c2w4x4: Float[Tensor, "B 4 4"] = torch.cat( + [self.c2w, torch.zeros_like(self.c2w[:, :1])], dim=1 + ) + self.c2w4x4[:, 3, 3] = 1.0 + + self.camera_position = camera_position + self.light_position = light_position + self.elevation_deg, self.azimuth_deg = elevation_deg, azimuth_deg + self.camera_distance = camera_distance + self.fovy = torch.deg2rad(torch.FloatTensor([self.cfg.default_fovy_deg])) + + self.heights: List[int] = ( + [self.cfg.height] if isinstance(self.cfg.height, int) else self.cfg.height + ) + self.widths: List[int] = ( + [self.cfg.width] if isinstance(self.cfg.width, int) else self.cfg.width + ) + assert len(self.heights) == len(self.widths) + self.resolution_milestones: List[int] + if len(self.heights) == 1 and len(self.widths) == 1: + if len(self.cfg.resolution_milestones) > 0: + threestudio.warn( + "Ignoring resolution_milestones since height and width are not changing" + ) + self.resolution_milestones = [-1] + else: + assert len(self.heights) == len(self.cfg.resolution_milestones) + 1 + self.resolution_milestones = [-1] + self.cfg.resolution_milestones + + self.directions_unit_focals = [ + get_ray_directions(H=height, W=width, focal=1.0) + for (height, width) in zip(self.heights, self.widths) + ] + self.focal_lengths = [ + 0.5 * height / torch.tan(0.5 * self.fovy) for height in self.heights + ] + + self.height: int = self.heights[0] + self.width: int = self.widths[0] + self.directions_unit_focal = self.directions_unit_focals[0] + self.focal_length = self.focal_lengths[0] + self.set_rays() + self.load_images() + self.prev_height = self.height + + def set_rays(self): + # get directions by dividing directions_unit_focal by focal length + directions: Float[Tensor, "1 H W 3"] = self.directions_unit_focal[None] + directions[:, :, :, :2] = directions[:, :, :, :2] / self.focal_length + + rays_o, rays_d = get_rays( + directions, + self.c2w, + keepdim=True, + noise_scale=self.cfg.rays_noise_scale, + normalize=self.cfg.rays_d_normalize, + ) + + proj_mtx: Float[Tensor, "4 4"] = get_projection_matrix( + self.fovy, self.width / self.height, 0.1, 100.0 + ) # FIXME: hard-coded near and far + mvp_mtx: Float[Tensor, "4 4"] = get_mvp_matrix(self.c2w, proj_mtx) + + self.rays_o, self.rays_d = rays_o, rays_d + self.mvp_mtx = mvp_mtx + + def load_images(self): + # load image + assert os.path.exists( + self.cfg.image_path + ), f"Could not find image {self.cfg.image_path}!" + rgba = cv2.cvtColor( + cv2.imread(self.cfg.image_path, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGRA2RGBA + ) + rgba = ( + cv2.resize( + rgba, (self.width, self.height), interpolation=cv2.INTER_AREA + ).astype(np.float32) + / 255.0 + ) + rgb = rgba[..., :3] + self.rgb: Float[Tensor, "1 H W 3"] = ( + torch.from_numpy(rgb).unsqueeze(0).contiguous().to(self.rank) + ) + self.mask: Float[Tensor, "1 H W 1"] = ( + torch.from_numpy(rgba[..., 3:] > 0.5).unsqueeze(0).to(self.rank) + ) + print( + f"[INFO] single image dataset: load image {self.cfg.image_path} {self.rgb.shape}" + ) + + # load depth + if self.cfg.requires_depth: + depth_path = self.cfg.image_path.replace("_rgba.png", "_depth.png") + assert os.path.exists(depth_path) + depth = cv2.imread(depth_path, cv2.IMREAD_UNCHANGED) + depth = cv2.resize( + depth, (self.width, self.height), interpolation=cv2.INTER_AREA + ) + self.depth: Float[Tensor, "1 H W 1"] = ( + torch.from_numpy(depth.astype(np.float32) / 255.0) + .unsqueeze(0) + .to(self.rank) + ) + print( + f"[INFO] single image dataset: load depth {depth_path} {self.depth.shape}" + ) + else: + self.depth = None + + # load normal + if self.cfg.requires_normal: + normal_path = self.cfg.image_path.replace("_rgba.png", "_normal.png") + assert os.path.exists(normal_path) + normal = cv2.imread(normal_path, cv2.IMREAD_UNCHANGED) + normal = cv2.resize( + normal, (self.width, self.height), interpolation=cv2.INTER_AREA + ) + self.normal: Float[Tensor, "1 H W 3"] = ( + torch.from_numpy(normal.astype(np.float32) / 255.0) + .unsqueeze(0) + .to(self.rank) + ) + print( + f"[INFO] single image dataset: load normal {normal_path} {self.normal.shape}" + ) + else: + self.normal = None + + def get_all_images(self): + return self.rgb + + def update_step_(self, epoch: int, global_step: int, on_load_weights: bool = False): + size_ind = bisect.bisect_right(self.resolution_milestones, global_step) - 1 + self.height = self.heights[size_ind] + if self.height == self.prev_height: + return + + self.prev_height = self.height + self.width = self.widths[size_ind] + self.directions_unit_focal = self.directions_unit_focals[size_ind] + self.focal_length = self.focal_lengths[size_ind] + threestudio.debug(f"Training height: {self.height}, width: {self.width}") + self.set_rays() + self.load_images() + + +class SingleImageIterableDataset(IterableDataset, SingleImageDataBase, Updateable): + def __init__(self, cfg: Any, split: str) -> None: + super().__init__() + self.setup(cfg, split) + + def collate(self, batch) -> Dict[str, Any]: + batch = { + "rays_o": self.rays_o, + "rays_d": self.rays_d, + "mvp_mtx": self.mvp_mtx, + "camera_positions": self.camera_position, + "light_positions": self.light_position, + "elevation": self.elevation_deg, + "azimuth": self.azimuth_deg, + "camera_distances": self.camera_distance, + "rgb": self.rgb, + "ref_depth": self.depth, + "ref_normal": self.normal, + "mask": self.mask, + "height": self.height, + "width": self.width, + "c2w": self.c2w4x4, + "fovy": self.fovy, + } + if self.cfg.use_random_camera: + batch["random_camera"] = self.random_pose_generator.collate(None) + + return batch + + def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False): + self.update_step_(epoch, global_step, on_load_weights) + self.random_pose_generator.update_step(epoch, global_step, on_load_weights) + + def __iter__(self): + while True: + yield {} + + +class SingleImageDataset(Dataset, SingleImageDataBase): + def __init__(self, cfg: Any, split: str) -> None: + super().__init__() + self.setup(cfg, split) + + def __len__(self): + return len(self.random_pose_generator) + + def __getitem__(self, index): + return self.random_pose_generator[index] + # if index == 0: + # return { + # 'rays_o': self.rays_o[0], + # 'rays_d': self.rays_d[0], + # 'mvp_mtx': self.mvp_mtx[0], + # 'camera_positions': self.camera_position[0], + # 'light_positions': self.light_position[0], + # 'elevation': self.elevation_deg[0], + # 'azimuth': self.azimuth_deg[0], + # 'camera_distances': self.camera_distance[0], + # 'rgb': self.rgb[0], + # 'depth': self.depth[0], + # 'mask': self.mask[0] + # } + # else: + # return self.random_pose_generator[index - 1] + + +@register("single-image-datamodule") +class SingleImageDataModule(pl.LightningDataModule): + cfg: SingleImageDataModuleConfig + + def __init__(self, cfg: Optional[Union[dict, DictConfig]] = None) -> None: + super().__init__() + self.cfg = parse_structured(SingleImageDataModuleConfig, cfg) + + def setup(self, stage=None) -> None: + if stage in [None, "fit"]: + self.train_dataset = SingleImageIterableDataset(self.cfg, "train") + if stage in [None, "fit", "validate"]: + self.val_dataset = SingleImageDataset(self.cfg, "val") + if stage in [None, "test", "predict"]: + self.test_dataset = SingleImageDataset(self.cfg, "test") + + def prepare_data(self): + pass + + def general_loader(self, dataset, batch_size, collate_fn=None) -> DataLoader: + return DataLoader( + dataset, num_workers=0, batch_size=batch_size, collate_fn=collate_fn + ) + + def train_dataloader(self) -> DataLoader: + return self.general_loader( + self.train_dataset, + batch_size=self.cfg.batch_size, + collate_fn=self.train_dataset.collate, + ) + + def val_dataloader(self) -> DataLoader: + return self.general_loader(self.val_dataset, batch_size=1) + + def test_dataloader(self) -> DataLoader: + return self.general_loader(self.test_dataset, batch_size=1) + + def predict_dataloader(self) -> DataLoader: + return self.general_loader(self.test_dataset, batch_size=1) diff --git a/threestudio/data/multiview.py b/threestudio/data/multiview.py new file mode 100644 index 0000000..8b72262 --- /dev/null +++ b/threestudio/data/multiview.py @@ -0,0 +1,435 @@ +import json +import math +import os +import random +from dataclasses import dataclass + +import cv2 +import numpy as np +import pytorch_lightning as pl +import torch +import torch.nn.functional as F +from scipy.spatial.transform import Rotation as Rot +from scipy.spatial.transform import Slerp +from torch.utils.data import DataLoader, Dataset, IterableDataset +from tqdm import tqdm + +import threestudio +from threestudio import register +from threestudio.utils.config import parse_structured +from threestudio.utils.ops import get_mvp_matrix, get_ray_directions, get_rays +from threestudio.utils.typing import * + + +def convert_pose(C2W): + flip_yz = torch.eye(4) + flip_yz[1, 1] = -1 + flip_yz[2, 2] = -1 + C2W = torch.matmul(C2W, flip_yz) + return C2W + + +def convert_proj(K, H, W, near, far): + return [ + [2 * K[0, 0] / W, -2 * K[0, 1] / W, (W - 2 * K[0, 2]) / W, 0], + [0, -2 * K[1, 1] / H, (H - 2 * K[1, 2]) / H, 0], + [0, 0, (-far - near) / (far - near), -2 * far * near / (far - near)], + [0, 0, -1, 0], + ] + + +def inter_pose(pose_0, pose_1, ratio): + pose_0 = pose_0.detach().cpu().numpy() + pose_1 = pose_1.detach().cpu().numpy() + pose_0 = np.linalg.inv(pose_0) + pose_1 = np.linalg.inv(pose_1) + rot_0 = pose_0[:3, :3] + rot_1 = pose_1[:3, :3] + rots = Rot.from_matrix(np.stack([rot_0, rot_1])) + key_times = [0, 1] + slerp = Slerp(key_times, rots) + rot = slerp(ratio) + pose = np.diag([1.0, 1.0, 1.0, 1.0]) + pose = pose.astype(np.float32) + pose[:3, :3] = rot.as_matrix() + pose[:3, 3] = ((1.0 - ratio) * pose_0 + ratio * pose_1)[:3, 3] + pose = np.linalg.inv(pose) + return pose + + +@dataclass +class MultiviewsDataModuleConfig: + dataroot: str = "" + train_downsample_resolution: int = 4 + eval_downsample_resolution: int = 4 + train_data_interval: int = 1 + eval_data_interval: int = 1 + batch_size: int = 1 + eval_batch_size: int = 1 + camera_layout: str = "around" + camera_distance: float = -1 + eval_interpolation: Optional[Tuple[int, int, int]] = None # (0, 1, 30) + + rays_d_normalize: bool = True + + +class MultiviewIterableDataset(IterableDataset): + def __init__(self, cfg: Any) -> None: + super().__init__() + self.cfg: MultiviewsDataModuleConfig = cfg + + assert self.cfg.batch_size == 1 + scale = self.cfg.train_downsample_resolution + + camera_dict = json.load( + open(os.path.join(self.cfg.dataroot, "transforms.json"), "r") + ) + assert camera_dict["camera_model"] == "OPENCV" + + frames = camera_dict["frames"] + frames = frames[:: self.cfg.train_data_interval] + frames_proj = [] + frames_c2w = [] + frames_position = [] + frames_direction = [] + frames_img = [] + + self.frame_w = frames[0]["w"] // scale + self.frame_h = frames[0]["h"] // scale + threestudio.info("Loading frames...") + self.n_frames = len(frames) + + c2w_list = [] + for frame in tqdm(frames): + extrinsic: Float[Tensor, "4 4"] = torch.as_tensor( + frame["transform_matrix"], dtype=torch.float32 + ) + c2w = extrinsic + c2w_list.append(c2w) + c2w_list = torch.stack(c2w_list, dim=0) + + if self.cfg.camera_layout == "around": + c2w_list[:, :3, 3] -= torch.mean(c2w_list[:, :3, 3], dim=0).unsqueeze(0) + elif self.cfg.camera_layout == "front": + assert self.cfg.camera_distance > 0 + c2w_list[:, :3, 3] -= torch.mean(c2w_list[:, :3, 3], dim=0).unsqueeze(0) + z_vector = torch.zeros(c2w_list.shape[0], 3, 1) + z_vector[:, 2, :] = -1 + rot_z_vector = c2w_list[:, :3, :3] @ z_vector + rot_z_vector = torch.mean(rot_z_vector, dim=0).unsqueeze(0) + c2w_list[:, :3, 3] -= rot_z_vector[:, :, 0] * self.cfg.camera_distance + else: + raise ValueError( + f"Unknown camera layout {self.cfg.camera_layout}. Now support only around and front." + ) + + for idx, frame in tqdm(enumerate(frames)): + intrinsic: Float[Tensor, "4 4"] = torch.eye(4) + intrinsic[0, 0] = frame["fl_x"] / scale + intrinsic[1, 1] = frame["fl_y"] / scale + intrinsic[0, 2] = frame["cx"] / scale + intrinsic[1, 2] = frame["cy"] / scale + + frame_path = os.path.join(self.cfg.dataroot, frame["file_path"]) + img = cv2.imread(frame_path)[:, :, ::-1].copy() + img = cv2.resize(img, (self.frame_w, self.frame_h)) + img: Float[Tensor, "H W 3"] = torch.FloatTensor(img) / 255 + frames_img.append(img) + + direction: Float[Tensor, "H W 3"] = get_ray_directions( + self.frame_h, + self.frame_w, + (intrinsic[0, 0], intrinsic[1, 1]), + (intrinsic[0, 2], intrinsic[1, 2]), + use_pixel_centers=False, + ) + + c2w = c2w_list[idx] + camera_position: Float[Tensor, "3"] = c2w[:3, 3:].reshape(-1) + + near = 0.1 + far = 1000.0 + proj = convert_proj(intrinsic, self.frame_h, self.frame_w, near, far) + proj: Float[Tensor, "4 4"] = torch.FloatTensor(proj) + frames_proj.append(proj) + frames_c2w.append(c2w) + frames_position.append(camera_position) + frames_direction.append(direction) + threestudio.info("Loaded frames.") + + self.frames_proj: Float[Tensor, "B 4 4"] = torch.stack(frames_proj, dim=0) + self.frames_c2w: Float[Tensor, "B 4 4"] = torch.stack(frames_c2w, dim=0) + self.frames_position: Float[Tensor, "B 3"] = torch.stack(frames_position, dim=0) + self.frames_direction: Float[Tensor, "B H W 3"] = torch.stack( + frames_direction, dim=0 + ) + self.frames_img: Float[Tensor, "B H W 3"] = torch.stack(frames_img, dim=0) + + self.rays_o, self.rays_d = get_rays( + self.frames_direction, + self.frames_c2w, + keepdim=True, + normalize=self.cfg.rays_d_normalize, + ) + self.mvp_mtx: Float[Tensor, "B 4 4"] = get_mvp_matrix( + self.frames_c2w, self.frames_proj + ) + self.light_positions: Float[Tensor, "B 3"] = torch.zeros_like( + self.frames_position + ) + + def __iter__(self): + while True: + yield {} + + def collate(self, batch): + index = torch.randint(0, self.n_frames, (1,)).item() + return { + "index": index, + "rays_o": self.rays_o[index : index + 1], + "rays_d": self.rays_d[index : index + 1], + "mvp_mtx": self.mvp_mtx[index : index + 1], + "c2w": self.frames_c2w[index : index + 1], + "camera_positions": self.frames_position[index : index + 1], + "light_positions": self.light_positions[index : index + 1], + "gt_rgb": self.frames_img[index : index + 1], + "height": self.frame_h, + "width": self.frame_w, + } + + +class MultiviewDataset(Dataset): + def __init__(self, cfg: Any, split: str) -> None: + super().__init__() + self.cfg: MultiviewsDataModuleConfig = cfg + + assert self.cfg.eval_batch_size == 1 + scale = self.cfg.eval_downsample_resolution + + camera_dict = json.load( + open(os.path.join(self.cfg.dataroot, "transforms.json"), "r") + ) + assert camera_dict["camera_model"] == "OPENCV" + + frames = camera_dict["frames"] + frames = frames[:: self.cfg.eval_data_interval] + frames_proj = [] + frames_c2w = [] + frames_position = [] + frames_direction = [] + frames_img = [] + + self.frame_w = frames[0]["w"] // scale + self.frame_h = frames[0]["h"] // scale + threestudio.info("Loading frames...") + self.n_frames = len(frames) + + c2w_list = [] + for frame in tqdm(frames): + extrinsic: Float[Tensor, "4 4"] = torch.as_tensor( + frame["transform_matrix"], dtype=torch.float32 + ) + c2w = extrinsic + c2w_list.append(c2w) + c2w_list = torch.stack(c2w_list, dim=0) + + if self.cfg.camera_layout == "around": + c2w_list[:, :3, 3] -= torch.mean(c2w_list[:, :3, 3], dim=0).unsqueeze(0) + elif self.cfg.camera_layout == "front": + assert self.cfg.camera_distance > 0 + c2w_list[:, :3, 3] -= torch.mean(c2w_list[:, :3, 3], dim=0).unsqueeze(0) + z_vector = torch.zeros(c2w_list.shape[0], 3, 1) + z_vector[:, 2, :] = -1 + rot_z_vector = c2w_list[:, :3, :3] @ z_vector + rot_z_vector = torch.mean(rot_z_vector, dim=0).unsqueeze(0) + c2w_list[:, :3, 3] -= rot_z_vector[:, :, 0] * self.cfg.camera_distance + else: + raise ValueError( + f"Unknown camera layout {self.cfg.camera_layout}. Now support only around and front." + ) + + if not (self.cfg.eval_interpolation is None): + idx0 = self.cfg.eval_interpolation[0] + idx1 = self.cfg.eval_interpolation[1] + eval_nums = self.cfg.eval_interpolation[2] + frame = frames[idx0] + intrinsic: Float[Tensor, "4 4"] = torch.eye(4) + intrinsic[0, 0] = frame["fl_x"] / scale + intrinsic[1, 1] = frame["fl_y"] / scale + intrinsic[0, 2] = frame["cx"] / scale + intrinsic[1, 2] = frame["cy"] / scale + for ratio in np.linspace(0, 1, eval_nums): + img: Float[Tensor, "H W 3"] = torch.zeros( + (self.frame_h, self.frame_w, 3) + ) + frames_img.append(img) + direction: Float[Tensor, "H W 3"] = get_ray_directions( + self.frame_h, + self.frame_w, + (intrinsic[0, 0], intrinsic[1, 1]), + (intrinsic[0, 2], intrinsic[1, 2]), + use_pixel_centers=False, + ) + + c2w = torch.FloatTensor( + inter_pose(c2w_list[idx0], c2w_list[idx1], ratio) + ) + camera_position: Float[Tensor, "3"] = c2w[:3, 3:].reshape(-1) + + near = 0.1 + far = 1000.0 + proj = convert_proj(intrinsic, self.frame_h, self.frame_w, near, far) + proj: Float[Tensor, "4 4"] = torch.FloatTensor(proj) + frames_proj.append(proj) + frames_c2w.append(c2w) + frames_position.append(camera_position) + frames_direction.append(direction) + else: + for idx, frame in tqdm(enumerate(frames)): + intrinsic: Float[Tensor, "4 4"] = torch.eye(4) + intrinsic[0, 0] = frame["fl_x"] / scale + intrinsic[1, 1] = frame["fl_y"] / scale + intrinsic[0, 2] = frame["cx"] / scale + intrinsic[1, 2] = frame["cy"] / scale + + frame_path = os.path.join(self.cfg.dataroot, frame["file_path"]) + img = cv2.imread(frame_path)[:, :, ::-1].copy() + img = cv2.resize(img, (self.frame_w, self.frame_h)) + img: Float[Tensor, "H W 3"] = torch.FloatTensor(img) / 255 + frames_img.append(img) + + direction: Float[Tensor, "H W 3"] = get_ray_directions( + self.frame_h, + self.frame_w, + (intrinsic[0, 0], intrinsic[1, 1]), + (intrinsic[0, 2], intrinsic[1, 2]), + use_pixel_centers=False, + ) + + c2w = c2w_list[idx] + camera_position: Float[Tensor, "3"] = c2w[:3, 3:].reshape(-1) + + near = 0.1 + far = 1000.0 + K = intrinsic + proj = [ + [ + 2 * K[0, 0] / self.frame_w, + -2 * K[0, 1] / self.frame_w, + (self.frame_w - 2 * K[0, 2]) / self.frame_w, + 0, + ], + [ + 0, + -2 * K[1, 1] / self.frame_h, + (self.frame_h - 2 * K[1, 2]) / self.frame_h, + 0, + ], + [ + 0, + 0, + (-far - near) / (far - near), + -2 * far * near / (far - near), + ], + [0, 0, -1, 0], + ] + proj: Float[Tensor, "4 4"] = torch.FloatTensor(proj) + frames_proj.append(proj) + frames_c2w.append(c2w) + frames_position.append(camera_position) + frames_direction.append(direction) + threestudio.info("Loaded frames.") + + self.frames_proj: Float[Tensor, "B 4 4"] = torch.stack(frames_proj, dim=0) + self.frames_c2w: Float[Tensor, "B 4 4"] = torch.stack(frames_c2w, dim=0) + self.frames_position: Float[Tensor, "B 3"] = torch.stack(frames_position, dim=0) + self.frames_direction: Float[Tensor, "B H W 3"] = torch.stack( + frames_direction, dim=0 + ) + self.frames_img: Float[Tensor, "B H W 3"] = torch.stack(frames_img, dim=0) + + self.rays_o, self.rays_d = get_rays( + self.frames_direction, + self.frames_c2w, + keepdim=True, + normalize=self.cfg.rays_d_normalize, + ) + self.mvp_mtx: Float[Tensor, "B 4 4"] = get_mvp_matrix( + self.frames_c2w, self.frames_proj + ) + self.light_positions: Float[Tensor, "B 3"] = torch.zeros_like( + self.frames_position + ) + + def __len__(self): + return self.frames_proj.shape[0] + + def __getitem__(self, index): + return { + "index": index, + "rays_o": self.rays_o[index], + "rays_d": self.rays_d[index], + "mvp_mtx": self.mvp_mtx[index], + "c2w": self.frames_c2w[index], + "camera_positions": self.frames_position[index], + "light_positions": self.light_positions[index], + "gt_rgb": self.frames_img[index], + } + + def __iter__(self): + while True: + yield {} + + def collate(self, batch): + batch = torch.utils.data.default_collate(batch) + batch.update({"height": self.frame_h, "width": self.frame_w}) + return batch + + +@register("multiview-camera-datamodule") +class MultiviewDataModule(pl.LightningDataModule): + cfg: MultiviewsDataModuleConfig + + def __init__(self, cfg: Optional[Union[dict, DictConfig]] = None) -> None: + super().__init__() + self.cfg = parse_structured(MultiviewsDataModuleConfig, cfg) + + def setup(self, stage=None) -> None: + if stage in [None, "fit"]: + self.train_dataset = MultiviewIterableDataset(self.cfg) + if stage in [None, "fit", "validate"]: + self.val_dataset = MultiviewDataset(self.cfg, "val") + if stage in [None, "test", "predict"]: + self.test_dataset = MultiviewDataset(self.cfg, "test") + + def prepare_data(self): + pass + + def general_loader(self, dataset, batch_size, collate_fn=None) -> DataLoader: + return DataLoader( + dataset, + num_workers=1, # type: ignore + batch_size=batch_size, + collate_fn=collate_fn, + ) + + def train_dataloader(self) -> DataLoader: + return self.general_loader( + self.train_dataset, batch_size=None, collate_fn=self.train_dataset.collate + ) + + def val_dataloader(self) -> DataLoader: + return self.general_loader( + self.val_dataset, batch_size=1, collate_fn=self.val_dataset.collate + ) + # return self.general_loader(self.train_dataset, batch_size=None, collate_fn=self.train_dataset.collate) + + def test_dataloader(self) -> DataLoader: + return self.general_loader( + self.test_dataset, batch_size=1, collate_fn=self.test_dataset.collate + ) + + def predict_dataloader(self) -> DataLoader: + return self.general_loader( + self.test_dataset, batch_size=1, collate_fn=self.test_dataset.collate + ) diff --git a/threestudio/data/uncond.py b/threestudio/data/uncond.py new file mode 100644 index 0000000..999ba55 --- /dev/null +++ b/threestudio/data/uncond.py @@ -0,0 +1,518 @@ +import bisect +import math +import random +from dataclasses import dataclass, field + +import numpy as np +import pytorch_lightning as pl +import torch +import torch.nn.functional as F +from torch.utils.data import DataLoader, Dataset, IterableDataset + +import threestudio +from threestudio import register +from threestudio.utils.base import Updateable +from threestudio.utils.config import parse_structured +from threestudio.utils.misc import get_device +from threestudio.utils.ops import ( + get_full_projection_matrix, + get_mvp_matrix, + get_projection_matrix, + get_ray_directions, + get_rays, +) +from threestudio.utils.typing import * + + +@dataclass +class RandomCameraDataModuleConfig: + # height, width, and batch_size should be Union[int, List[int]] + # but OmegaConf does not support Union of containers + height: Any = 64 + width: Any = 64 + batch_size: Any = 1 + resolution_milestones: List[int] = field(default_factory=lambda: []) + eval_height: int = 512 + eval_width: int = 512 + eval_batch_size: int = 1 + n_val_views: int = 1 + n_test_views: int = 120 + elevation_range: Tuple[float, float] = (-10, 90) + azimuth_range: Tuple[float, float] = (-180, 180) + camera_distance_range: Tuple[float, float] = (1, 1.5) + fovy_range: Tuple[float, float] = ( + 40, + 70, + ) # in degrees, in vertical direction (along height) + camera_perturb: float = 0.1 + center_perturb: float = 0.2 + up_perturb: float = 0.02 + light_position_perturb: float = 1.0 + light_distance_range: Tuple[float, float] = (0.8, 1.5) + eval_elevation_deg: float = 15.0 + eval_camera_distance: float = 1.5 + eval_fovy_deg: float = 70.0 + light_sample_strategy: str = "dreamfusion" + batch_uniform_azimuth: bool = True + progressive_until: int = 0 # progressive ranges for elevation, azimuth, r, fovy + + rays_d_normalize: bool = True + + +class RandomCameraIterableDataset(IterableDataset, Updateable): + def __init__(self, cfg: Any) -> None: + super().__init__() + self.cfg: RandomCameraDataModuleConfig = cfg + self.heights: List[int] = ( + [self.cfg.height] if isinstance(self.cfg.height, int) else self.cfg.height + ) + self.widths: List[int] = ( + [self.cfg.width] if isinstance(self.cfg.width, int) else self.cfg.width + ) + self.batch_sizes: List[int] = ( + [self.cfg.batch_size] + if isinstance(self.cfg.batch_size, int) + else self.cfg.batch_size + ) + assert len(self.heights) == len(self.widths) == len(self.batch_sizes) + self.resolution_milestones: List[int] + if ( + len(self.heights) == 1 + and len(self.widths) == 1 + and len(self.batch_sizes) == 1 + ): + if len(self.cfg.resolution_milestones) > 0: + threestudio.warn( + "Ignoring resolution_milestones since height and width are not changing" + ) + self.resolution_milestones = [-1] + else: + assert len(self.heights) == len(self.cfg.resolution_milestones) + 1 + self.resolution_milestones = [-1] + self.cfg.resolution_milestones + + self.directions_unit_focals = [ + get_ray_directions(H=height, W=width, focal=1.0) + for (height, width) in zip(self.heights, self.widths) + ] + self.height: int = self.heights[0] + self.width: int = self.widths[0] + self.batch_size: int = self.batch_sizes[0] + self.directions_unit_focal = self.directions_unit_focals[0] + self.elevation_range = self.cfg.elevation_range + self.azimuth_range = self.cfg.azimuth_range + self.camera_distance_range = self.cfg.camera_distance_range + self.fovy_range = self.cfg.fovy_range + + def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False): + size_ind = bisect.bisect_right(self.resolution_milestones, global_step) - 1 + self.height = self.heights[size_ind] + self.width = self.widths[size_ind] + self.batch_size = self.batch_sizes[size_ind] + self.directions_unit_focal = self.directions_unit_focals[size_ind] + threestudio.debug( + f"Training height: {self.height}, width: {self.width}, batch_size: {self.batch_size}" + ) + # progressive view + self.progressive_view(global_step) + + def __iter__(self): + while True: + yield {} + + def progressive_view(self, global_step): + r = min(1.0, global_step / (self.cfg.progressive_until + 1)) + self.elevation_range = [ + (1 - r) * self.cfg.eval_elevation_deg + r * self.cfg.elevation_range[0], + (1 - r) * self.cfg.eval_elevation_deg + r * self.cfg.elevation_range[1], + ] + self.azimuth_range = [ + (1 - r) * 0.0 + r * self.cfg.azimuth_range[0], + (1 - r) * 0.0 + r * self.cfg.azimuth_range[1], + ] + # self.camera_distance_range = [ + # (1 - r) * self.cfg.eval_camera_distance + # + r * self.cfg.camera_distance_range[0], + # (1 - r) * self.cfg.eval_camera_distance + # + r * self.cfg.camera_distance_range[1], + # ] + # self.fovy_range = [ + # (1 - r) * self.cfg.eval_fovy_deg + r * self.cfg.fovy_range[0], + # (1 - r) * self.cfg.eval_fovy_deg + r * self.cfg.fovy_range[1], + # ] + + def collate(self, batch) -> Dict[str, Any]: + # sample elevation angles + elevation_deg: Float[Tensor, "B"] + elevation: Float[Tensor, "B"] + if random.random() < 0.5: + # sample elevation angles uniformly with a probability 0.5 (biased towards poles) + elevation_deg = ( + torch.rand(self.batch_size) + * (self.elevation_range[1] - self.elevation_range[0]) + + self.elevation_range[0] + ) + elevation = elevation_deg * math.pi / 180 + else: + # otherwise sample uniformly on sphere + elevation_range_percent = [ + self.elevation_range[0] / 180.0 * math.pi, + self.elevation_range[1] / 180.0 * math.pi, + ] + # inverse transform sampling + elevation = torch.asin( + ( + torch.rand(self.batch_size) + * ( + math.sin(elevation_range_percent[1]) + - math.sin(elevation_range_percent[0]) + ) + + math.sin(elevation_range_percent[0]) + ) + ) + elevation_deg = elevation / math.pi * 180.0 + + # sample azimuth angles from a uniform distribution bounded by azimuth_range + azimuth_deg: Float[Tensor, "B"] + if self.cfg.batch_uniform_azimuth: + # ensures sampled azimuth angles in a batch cover the whole range + azimuth_deg = ( + torch.rand(self.batch_size) + torch.arange(self.batch_size) + ) / self.batch_size * ( + self.azimuth_range[1] - self.azimuth_range[0] + ) + self.azimuth_range[ + 0 + ] + else: + # simple random sampling + azimuth_deg = ( + torch.rand(self.batch_size) + * (self.azimuth_range[1] - self.azimuth_range[0]) + + self.azimuth_range[0] + ) + azimuth = azimuth_deg * math.pi / 180 + + # sample distances from a uniform distribution bounded by distance_range + camera_distances: Float[Tensor, "B"] = ( + torch.rand(self.batch_size) + * (self.camera_distance_range[1] - self.camera_distance_range[0]) + + self.camera_distance_range[0] + ) + + # convert spherical coordinates to cartesian coordinates + # right hand coordinate system, x back, y right, z up + # elevation in (-90, 90), azimuth from +x to +y in (-180, 180) + camera_positions: Float[Tensor, "B 3"] = torch.stack( + [ + camera_distances * torch.cos(elevation) * torch.cos(azimuth), + camera_distances * torch.cos(elevation) * torch.sin(azimuth), + camera_distances * torch.sin(elevation), + ], + dim=-1, + ) + + # default scene center at origin + center: Float[Tensor, "B 3"] = torch.zeros_like(camera_positions) + # default camera up direction as +z + up: Float[Tensor, "B 3"] = torch.as_tensor([0, 0, 1], dtype=torch.float32)[ + None, : + ].repeat(self.batch_size, 1) + + # sample camera perturbations from a uniform distribution [-camera_perturb, camera_perturb] + camera_perturb: Float[Tensor, "B 3"] = ( + torch.rand(self.batch_size, 3) * 2 * self.cfg.camera_perturb + - self.cfg.camera_perturb + ) + camera_positions = camera_positions + camera_perturb + # sample center perturbations from a normal distribution with mean 0 and std center_perturb + center_perturb: Float[Tensor, "B 3"] = ( + torch.randn(self.batch_size, 3) * self.cfg.center_perturb + ) + center = center + center_perturb + # sample up perturbations from a normal distribution with mean 0 and std up_perturb + up_perturb: Float[Tensor, "B 3"] = ( + torch.randn(self.batch_size, 3) * self.cfg.up_perturb + ) + up = up + up_perturb + + # sample fovs from a uniform distribution bounded by fov_range + fovy_deg: Float[Tensor, "B"] = ( + torch.rand(self.batch_size) * (self.fovy_range[1] - self.fovy_range[0]) + + self.fovy_range[0] + ) + fovy = fovy_deg * math.pi / 180 + + # sample light distance from a uniform distribution bounded by light_distance_range + light_distances: Float[Tensor, "B"] = ( + torch.rand(self.batch_size) + * (self.cfg.light_distance_range[1] - self.cfg.light_distance_range[0]) + + self.cfg.light_distance_range[0] + ) + + if self.cfg.light_sample_strategy == "dreamfusion": + # sample light direction from a normal distribution with mean camera_position and std light_position_perturb + light_direction: Float[Tensor, "B 3"] = F.normalize( + camera_positions + + torch.randn(self.batch_size, 3) * self.cfg.light_position_perturb, + dim=-1, + ) + # get light position by scaling light direction by light distance + light_positions: Float[Tensor, "B 3"] = ( + light_direction * light_distances[:, None] + ) + elif self.cfg.light_sample_strategy == "magic3d": + # sample light direction within restricted angle range (pi/3) + local_z = F.normalize(camera_positions, dim=-1) + local_x = F.normalize( + torch.stack( + [local_z[:, 1], -local_z[:, 0], torch.zeros_like(local_z[:, 0])], + dim=-1, + ), + dim=-1, + ) + local_y = F.normalize(torch.cross(local_z, local_x, dim=-1), dim=-1) + rot = torch.stack([local_x, local_y, local_z], dim=-1) + light_azimuth = ( + torch.rand(self.batch_size) * math.pi * 2 - math.pi + ) # [-pi, pi] + light_elevation = ( + torch.rand(self.batch_size) * math.pi / 3 + math.pi / 6 + ) # [pi/6, pi/2] + light_positions_local = torch.stack( + [ + light_distances + * torch.cos(light_elevation) + * torch.cos(light_azimuth), + light_distances + * torch.cos(light_elevation) + * torch.sin(light_azimuth), + light_distances * torch.sin(light_elevation), + ], + dim=-1, + ) + light_positions = (rot @ light_positions_local[:, :, None])[:, :, 0] + else: + raise ValueError( + f"Unknown light sample strategy: {self.cfg.light_sample_strategy}" + ) + + lookat: Float[Tensor, "B 3"] = F.normalize(center - camera_positions, dim=-1) + right: Float[Tensor, "B 3"] = F.normalize(torch.cross(lookat, up), dim=-1) + up = F.normalize(torch.cross(right, lookat), dim=-1) + c2w3x4: Float[Tensor, "B 3 4"] = torch.cat( + [torch.stack([right, up, -lookat], dim=-1), camera_positions[:, :, None]], + dim=-1, + ) + c2w: Float[Tensor, "B 4 4"] = torch.cat( + [c2w3x4, torch.zeros_like(c2w3x4[:, :1])], dim=1 + ) + c2w[:, 3, 3] = 1.0 + + # get directions by dividing directions_unit_focal by focal length + focal_length: Float[Tensor, "B"] = 0.5 * self.height / torch.tan(0.5 * fovy) + directions: Float[Tensor, "B H W 3"] = self.directions_unit_focal[ + None, :, :, : + ].repeat(self.batch_size, 1, 1, 1) + directions[:, :, :, :2] = ( + directions[:, :, :, :2] / focal_length[:, None, None, None] + ) + + # Importance note: the returned rays_d MUST be normalized! + rays_o, rays_d = get_rays( + directions, c2w, keepdim=True, normalize=self.cfg.rays_d_normalize + ) + + self.proj_mtx: Float[Tensor, "B 4 4"] = get_projection_matrix( + fovy, self.width / self.height, 0.01, 100.0 + ) # FIXME: hard-coded near and far + mvp_mtx: Float[Tensor, "B 4 4"] = get_mvp_matrix(c2w, self.proj_mtx) + self.fovy = fovy + + return { + "rays_o": rays_o, + "rays_d": rays_d, + "mvp_mtx": mvp_mtx, + "camera_positions": camera_positions, + "c2w": c2w, + "light_positions": light_positions, + "elevation": elevation_deg, + "azimuth": azimuth_deg, + "camera_distances": camera_distances, + "height": self.height, + "width": self.width, + "fovy": self.fovy, + "proj_mtx": self.proj_mtx, + } + + +class RandomCameraDataset(Dataset): + def __init__(self, cfg: Any, split: str) -> None: + super().__init__() + self.cfg: RandomCameraDataModuleConfig = cfg + self.split = split + + if split == "val": + self.n_views = self.cfg.n_val_views + else: + self.n_views = self.cfg.n_test_views + + azimuth_deg: Float[Tensor, "B"] + if self.split == "val": + # make sure the first and last view are not the same + azimuth_deg = torch.linspace(0, 360.0, self.n_views + 1)[: self.n_views] + else: + azimuth_deg = torch.linspace(0, 360.0, self.n_views) + elevation_deg: Float[Tensor, "B"] = torch.full_like( + azimuth_deg, self.cfg.eval_elevation_deg + ) + camera_distances: Float[Tensor, "B"] = torch.full_like( + elevation_deg, self.cfg.eval_camera_distance + ) + + elevation = elevation_deg * math.pi / 180 + azimuth = azimuth_deg * math.pi / 180 + + # convert spherical coordinates to cartesian coordinates + # right hand coordinate system, x back, y right, z up + # elevation in (-90, 90), azimuth from +x to +y in (-180, 180) + camera_positions: Float[Tensor, "B 3"] = torch.stack( + [ + camera_distances * torch.cos(elevation) * torch.cos(azimuth), + camera_distances * torch.cos(elevation) * torch.sin(azimuth), + camera_distances * torch.sin(elevation), + ], + dim=-1, + ) + + # default scene center at origin + center: Float[Tensor, "B 3"] = torch.zeros_like(camera_positions) + # default camera up direction as +z + up: Float[Tensor, "B 3"] = torch.as_tensor([0, 0, 1], dtype=torch.float32)[ + None, : + ].repeat(self.cfg.eval_batch_size, 1) + + fovy_deg: Float[Tensor, "B"] = torch.full_like( + elevation_deg, self.cfg.eval_fovy_deg + ) + fovy = fovy_deg * math.pi / 180 + light_positions: Float[Tensor, "B 3"] = camera_positions + + lookat: Float[Tensor, "B 3"] = F.normalize(center - camera_positions, dim=-1) + right: Float[Tensor, "B 3"] = F.normalize(torch.cross(lookat, up), dim=-1) + up = F.normalize(torch.cross(right, lookat), dim=-1) + c2w3x4: Float[Tensor, "B 3 4"] = torch.cat( + [torch.stack([right, up, -lookat], dim=-1), camera_positions[:, :, None]], + dim=-1, + ) + c2w: Float[Tensor, "B 4 4"] = torch.cat( + [c2w3x4, torch.zeros_like(c2w3x4[:, :1])], dim=1 + ) + c2w[:, 3, 3] = 1.0 + + # get directions by dividing directions_unit_focal by focal length + focal_length: Float[Tensor, "B"] = ( + 0.5 * self.cfg.eval_height / torch.tan(0.5 * fovy) + ) + directions_unit_focal = get_ray_directions( + H=self.cfg.eval_height, W=self.cfg.eval_width, focal=1.0 + ) + directions: Float[Tensor, "B H W 3"] = directions_unit_focal[ + None, :, :, : + ].repeat(self.n_views, 1, 1, 1) + directions[:, :, :, :2] = ( + directions[:, :, :, :2] / focal_length[:, None, None, None] + ) + + rays_o, rays_d = get_rays( + directions, c2w, keepdim=True, normalize=self.cfg.rays_d_normalize + ) + self.proj_mtx: Float[Tensor, "B 4 4"] = get_projection_matrix( + fovy, self.cfg.eval_width / self.cfg.eval_height, 0.01, 100.0 + ) # FIXME: hard-coded near and far + mvp_mtx: Float[Tensor, "B 4 4"] = get_mvp_matrix(c2w, self.proj_mtx) + + self.rays_o, self.rays_d = rays_o, rays_d + self.mvp_mtx = mvp_mtx + self.c2w = c2w + self.camera_positions = camera_positions + self.light_positions = light_positions + self.elevation, self.azimuth = elevation, azimuth + self.elevation_deg, self.azimuth_deg = elevation_deg, azimuth_deg + self.camera_distances = camera_distances + self.fovy = fovy + + def __len__(self): + return self.n_views + + def __getitem__(self, index): + return { + "index": index, + "rays_o": self.rays_o[index], + "rays_d": self.rays_d[index], + "mvp_mtx": self.mvp_mtx[index], + "c2w": self.c2w[index], + "camera_positions": self.camera_positions[index], + "light_positions": self.light_positions[index], + "elevation": self.elevation_deg[index], + "azimuth": self.azimuth_deg[index], + "camera_distances": self.camera_distances[index], + "height": self.cfg.eval_height, + "width": self.cfg.eval_width, + "fovy": self.fovy[index], + "proj_mtx": self.proj_mtx[index], + } + + def collate(self, batch): + batch = torch.utils.data.default_collate(batch) + batch.update({"height": self.cfg.eval_height, "width": self.cfg.eval_width}) + return batch + + +@register("random-camera-datamodule") +class RandomCameraDataModule(pl.LightningDataModule): + cfg: RandomCameraDataModuleConfig + + def __init__(self, cfg: Optional[Union[dict, DictConfig]] = None) -> None: + super().__init__() + self.cfg = parse_structured(RandomCameraDataModuleConfig, cfg) + + def setup(self, stage=None) -> None: + if stage in [None, "fit"]: + self.train_dataset = RandomCameraIterableDataset(self.cfg) + if stage in [None, "fit", "validate"]: + self.val_dataset = RandomCameraDataset(self.cfg, "val") + if stage in [None, "test", "predict"]: + self.test_dataset = RandomCameraDataset(self.cfg, "test") + + def prepare_data(self): + pass + + def general_loader(self, dataset, batch_size, collate_fn=None) -> DataLoader: + return DataLoader( + dataset, + # very important to disable multi-processing if you want to change self attributes at runtime! + # (for example setting self.width and self.height in update_step) + num_workers=0, # type: ignore + batch_size=batch_size, + collate_fn=collate_fn, + ) + + def train_dataloader(self) -> DataLoader: + return self.general_loader( + self.train_dataset, batch_size=None, collate_fn=self.train_dataset.collate + ) + + def val_dataloader(self) -> DataLoader: + return self.general_loader( + self.val_dataset, batch_size=1, collate_fn=self.val_dataset.collate + ) + # return self.general_loader(self.train_dataset, batch_size=None, collate_fn=self.train_dataset.collate) + + def test_dataloader(self) -> DataLoader: + return self.general_loader( + self.test_dataset, batch_size=1, collate_fn=self.test_dataset.collate + ) + + def predict_dataloader(self) -> DataLoader: + return self.general_loader( + self.test_dataset, batch_size=1, collate_fn=self.test_dataset.collate + ) diff --git a/threestudio/data/uncond_eff.py b/threestudio/data/uncond_eff.py new file mode 100644 index 0000000..a1ac04f --- /dev/null +++ b/threestudio/data/uncond_eff.py @@ -0,0 +1,441 @@ +import bisect +import math +import random +from dataclasses import dataclass, field + +import numpy as np +import pytorch_lightning as pl +import torch +import torch.nn.functional as F +from torch.utils.data import DataLoader, Dataset, IterableDataset + +import threestudio +from threestudio import register +from threestudio.data.uncond import RandomCameraDataset +from threestudio.utils.base import Updateable +from threestudio.utils.config import parse_structured +from threestudio.utils.misc import get_device +from threestudio.utils.ops import ( + get_full_projection_matrix, + get_mvp_matrix, + get_projection_matrix, + get_ray_directions, + get_rays, + mask_ray_directions, +) +from threestudio.utils.typing import * + + +@dataclass +class EffRandomCameraDataModuleConfig: + # height, width, and batch_size should be Union[int, List[int]] + # but OmegaConf does not support Union of containers + height: Any = 128 + width: Any = 128 + sample_height: Any = 64 + sample_width: Any = 64 + batch_size: Any = 1 + resolution_milestones: List[int] = field(default_factory=lambda: []) + eval_height: int = 512 + eval_width: int = 512 + eval_batch_size: int = 1 + n_val_views: int = 1 + n_test_views: int = 120 + elevation_range: Tuple[float, float] = (-10, 90) + azimuth_range: Tuple[float, float] = (-180, 180) + camera_distance_range: Tuple[float, float] = (1, 1.5) + fovy_range: Tuple[float, float] = ( + 40, + 70, + ) # in degrees, in vertical direction (along height) + camera_perturb: float = 0.1 + center_perturb: float = 0.2 + up_perturb: float = 0.02 + light_position_perturb: float = 1.0 + light_distance_range: Tuple[float, float] = (0.8, 1.5) + eval_elevation_deg: float = 15.0 + eval_camera_distance: float = 1.5 + eval_fovy_deg: float = 70.0 + light_sample_strategy: str = "dreamfusion" + batch_uniform_azimuth: bool = True + progressive_until: int = 0 # progressive ranges for elevation, azimuth, r, fovy + + rays_d_normalize: bool = True + + +class EffRandomCameraIterableDataset(IterableDataset, Updateable): + def __init__(self, cfg: Any) -> None: + super().__init__() + self.cfg: EffRandomCameraDataModuleConfig = cfg + self.heights: List[int] = ( + [self.cfg.height] if isinstance(self.cfg.height, int) else self.cfg.height + ) + self.widths: List[int] = ( + [self.cfg.width] if isinstance(self.cfg.width, int) else self.cfg.width + ) + self.sample_heights: List[int] = ( + [self.cfg.sample_height] + if isinstance(self.cfg.sample_height, int) + else self.cfg.sample_height + ) + self.sample_widths: List[int] = ( + [self.cfg.sample_width] + if isinstance(self.cfg.sample_width, int) + else self.cfg.sample_width + ) + self.batch_sizes: List[int] = ( + [self.cfg.batch_size] + if isinstance(self.cfg.batch_size, int) + else self.cfg.batch_size + ) + assert ( + len(self.heights) + == len(self.widths) + == len(self.batch_sizes) + == len(self.sample_heights) + == len(self.sample_widths) + ) + self.resolution_milestones: List[int] + if ( + len(self.heights) == 1 + and len(self.widths) == 1 + and len(self.batch_sizes) == 1 + and len(self.sample_heights) == 1 + and len(self.sample_widths) == 1 + ): + if len(self.cfg.resolution_milestones) > 0: + threestudio.warn( + "Ignoring resolution_milestones since height and width are not changing" + ) + self.resolution_milestones = [-1] + else: + assert len(self.heights) == len(self.cfg.resolution_milestones) + 1 + self.resolution_milestones = [-1] + self.cfg.resolution_milestones + + self.directions_unit_focals = [ + get_ray_directions(H=height, W=width, focal=1.0) + for (height, width) in zip(self.heights, self.widths) + ] + + self.efficiency_masks = [ + (mask_ray_directions(H, W, s_H, s_W)) + for (H, W, s_H, s_W) in zip( + self.heights, self.widths, self.sample_heights, self.sample_widths + ) + ] + self.directions_unit_focals = [ + (self.directions_unit_focals[i].view(-1, 3)[self.efficiency_masks[i]]).view( + self.sample_heights[i], self.sample_widths[i], 3 + ) + for i in range(len(self.heights)) + ] + + self.height: int = self.heights[0] + self.width: int = self.widths[0] + self.sample_height: int = self.sample_heights[0] + self.sample_width: int = self.sample_widths[0] + self.batch_size: int = self.batch_sizes[0] + self.directions_unit_focal = self.directions_unit_focals[0] + self.efficiency_mask = self.efficiency_masks[0] + self.elevation_range = self.cfg.elevation_range + self.azimuth_range = self.cfg.azimuth_range + self.camera_distance_range = self.cfg.camera_distance_range + self.fovy_range = self.cfg.fovy_range + + def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False): + size_ind = bisect.bisect_right(self.resolution_milestones, global_step) - 1 + self.height = self.heights[size_ind] + self.width = self.widths[size_ind] + self.sample_height = self.sample_heights[size_ind] + self.sample_width = self.sample_widths[size_ind] + self.batch_size = self.batch_sizes[size_ind] + self.directions_unit_focal = self.directions_unit_focals[size_ind] + self.efficiency_mask = self.efficiency_masks[size_ind] + threestudio.debug( + f"Training height: {self.height}, width: {self.width}, batch_size: {self.batch_size}" + ) + # progressive view + self.progressive_view(global_step) + + def __iter__(self): + while True: + yield {} + + def progressive_view(self, global_step): + r = min(1.0, global_step / (self.cfg.progressive_until + 1)) + self.elevation_range = [ + (1 - r) * self.cfg.eval_elevation_deg + r * self.cfg.elevation_range[0], + (1 - r) * self.cfg.eval_elevation_deg + r * self.cfg.elevation_range[1], + ] + self.azimuth_range = [ + (1 - r) * 0.0 + r * self.cfg.azimuth_range[0], + (1 - r) * 0.0 + r * self.cfg.azimuth_range[1], + ] + # self.camera_distance_range = [ + # (1 - r) * self.cfg.eval_camera_distance + # + r * self.cfg.camera_distance_range[0], + # (1 - r) * self.cfg.eval_camera_distance + # + r * self.cfg.camera_distance_range[1], + # ] + # self.fovy_range = [ + # (1 - r) * self.cfg.eval_fovy_deg + r * self.cfg.fovy_range[0], + # (1 - r) * self.cfg.eval_fovy_deg + r * self.cfg.fovy_range[1], + # ] + + def collate(self, batch) -> Dict[str, Any]: + # sample elevation angles + elevation_deg: Float[Tensor, "B"] + elevation: Float[Tensor, "B"] + if random.random() < 0.5: + # sample elevation angles uniformly with a probability 0.5 (biased towards poles) + elevation_deg = ( + torch.rand(self.batch_size) + * (self.elevation_range[1] - self.elevation_range[0]) + + self.elevation_range[0] + ) + elevation = elevation_deg * math.pi / 180 + else: + # otherwise sample uniformly on sphere + elevation_range_percent = [ + self.elevation_range[0] / 180.0 * math.pi, + self.elevation_range[1] / 180.0 * math.pi, + ] + # inverse transform sampling + elevation = torch.asin( + ( + torch.rand(self.batch_size) + * ( + math.sin(elevation_range_percent[1]) + - math.sin(elevation_range_percent[0]) + ) + + math.sin(elevation_range_percent[0]) + ) + ) + elevation_deg = elevation / math.pi * 180.0 + + # sample azimuth angles from a uniform distribution bounded by azimuth_range + azimuth_deg: Float[Tensor, "B"] + if self.cfg.batch_uniform_azimuth: + # ensures sampled azimuth angles in a batch cover the whole range + azimuth_deg = ( + torch.rand(self.batch_size) + torch.arange(self.batch_size) + ) / self.batch_size * ( + self.azimuth_range[1] - self.azimuth_range[0] + ) + self.azimuth_range[ + 0 + ] + else: + # simple random sampling + azimuth_deg = ( + torch.rand(self.batch_size) + * (self.azimuth_range[1] - self.azimuth_range[0]) + + self.azimuth_range[0] + ) + azimuth = azimuth_deg * math.pi / 180 + + # sample distances from a uniform distribution bounded by distance_range + camera_distances: Float[Tensor, "B"] = ( + torch.rand(self.batch_size) + * (self.camera_distance_range[1] - self.camera_distance_range[0]) + + self.camera_distance_range[0] + ) + + # convert spherical coordinates to cartesian coordinates + # right hand coordinate system, x back, y right, z up + # elevation in (-90, 90), azimuth from +x to +y in (-180, 180) + camera_positions: Float[Tensor, "B 3"] = torch.stack( + [ + camera_distances * torch.cos(elevation) * torch.cos(azimuth), + camera_distances * torch.cos(elevation) * torch.sin(azimuth), + camera_distances * torch.sin(elevation), + ], + dim=-1, + ) + + # default scene center at origin + center: Float[Tensor, "B 3"] = torch.zeros_like(camera_positions) + # default camera up direction as +z + up: Float[Tensor, "B 3"] = torch.as_tensor([0, 0, 1], dtype=torch.float32)[ + None, : + ].repeat(self.batch_size, 1) + + # sample camera perturbations from a uniform distribution [-camera_perturb, camera_perturb] + camera_perturb: Float[Tensor, "B 3"] = ( + torch.rand(self.batch_size, 3) * 2 * self.cfg.camera_perturb + - self.cfg.camera_perturb + ) + camera_positions = camera_positions + camera_perturb + # sample center perturbations from a normal distribution with mean 0 and std center_perturb + center_perturb: Float[Tensor, "B 3"] = ( + torch.randn(self.batch_size, 3) * self.cfg.center_perturb + ) + center = center + center_perturb + # sample up perturbations from a normal distribution with mean 0 and std up_perturb + up_perturb: Float[Tensor, "B 3"] = ( + torch.randn(self.batch_size, 3) * self.cfg.up_perturb + ) + up = up + up_perturb + + # sample fovs from a uniform distribution bounded by fov_range + fovy_deg: Float[Tensor, "B"] = ( + torch.rand(self.batch_size) * (self.fovy_range[1] - self.fovy_range[0]) + + self.fovy_range[0] + ) + fovy = fovy_deg * math.pi / 180 + + # sample light distance from a uniform distribution bounded by light_distance_range + light_distances: Float[Tensor, "B"] = ( + torch.rand(self.batch_size) + * (self.cfg.light_distance_range[1] - self.cfg.light_distance_range[0]) + + self.cfg.light_distance_range[0] + ) + + if self.cfg.light_sample_strategy == "dreamfusion": + # sample light direction from a normal distribution with mean camera_position and std light_position_perturb + light_direction: Float[Tensor, "B 3"] = F.normalize( + camera_positions + + torch.randn(self.batch_size, 3) * self.cfg.light_position_perturb, + dim=-1, + ) + # get light position by scaling light direction by light distance + light_positions: Float[Tensor, "B 3"] = ( + light_direction * light_distances[:, None] + ) + elif self.cfg.light_sample_strategy == "magic3d": + # sample light direction within restricted angle range (pi/3) + local_z = F.normalize(camera_positions, dim=-1) + local_x = F.normalize( + torch.stack( + [local_z[:, 1], -local_z[:, 0], torch.zeros_like(local_z[:, 0])], + dim=-1, + ), + dim=-1, + ) + local_y = F.normalize(torch.cross(local_z, local_x, dim=-1), dim=-1) + rot = torch.stack([local_x, local_y, local_z], dim=-1) + light_azimuth = ( + torch.rand(self.batch_size) * math.pi * 2 - math.pi + ) # [-pi, pi] + light_elevation = ( + torch.rand(self.batch_size) * math.pi / 3 + math.pi / 6 + ) # [pi/6, pi/2] + light_positions_local = torch.stack( + [ + light_distances + * torch.cos(light_elevation) + * torch.cos(light_azimuth), + light_distances + * torch.cos(light_elevation) + * torch.sin(light_azimuth), + light_distances * torch.sin(light_elevation), + ], + dim=-1, + ) + light_positions = (rot @ light_positions_local[:, :, None])[:, :, 0] + else: + raise ValueError( + f"Unknown light sample strategy: {self.cfg.light_sample_strategy}" + ) + + lookat: Float[Tensor, "B 3"] = F.normalize(center - camera_positions, dim=-1) + right: Float[Tensor, "B 3"] = F.normalize(torch.cross(lookat, up), dim=-1) + up = F.normalize(torch.cross(right, lookat), dim=-1) + c2w3x4: Float[Tensor, "B 3 4"] = torch.cat( + [torch.stack([right, up, -lookat], dim=-1), camera_positions[:, :, None]], + dim=-1, + ) + c2w: Float[Tensor, "B 4 4"] = torch.cat( + [c2w3x4, torch.zeros_like(c2w3x4[:, :1])], dim=1 + ) + c2w[:, 3, 3] = 1.0 + + # get directions by dividing directions_unit_focal by focal length + focal_length: Float[Tensor, "B"] = 0.5 * self.height / torch.tan(0.5 * fovy) + directions: Float[Tensor, "B H W 3"] = self.directions_unit_focal[ + None, :, :, : + ].repeat(self.batch_size, 1, 1, 1) + directions[:, :, :, :2] = ( + directions[:, :, :, :2] / focal_length[:, None, None, None] + ) + + # Importance note: the returned rays_d MUST be normalized! + ### Efficiency masking added here + rays_o, rays_d = get_rays( + directions, c2w, keepdim=True, normalize=self.cfg.rays_d_normalize + ) + + self.proj_mtx: Float[Tensor, "B 4 4"] = get_projection_matrix( + fovy, self.width / self.height, 0.01, 100.0 + ) # FIXME: hard-coded near and far + mvp_mtx: Float[Tensor, "B 4 4"] = get_mvp_matrix(c2w, self.proj_mtx) + self.fovy = fovy + + return { + "rays_o": rays_o, + "rays_d": rays_d, + "efficiency_mask": self.efficiency_mask, + "mvp_mtx": mvp_mtx, + "camera_positions": camera_positions, + "c2w": c2w, + "light_positions": light_positions, + "elevation": elevation_deg, + "azimuth": azimuth_deg, + "camera_distances": camera_distances, + "height": self.height, + "width": self.width, + "sample_height": self.sample_height, + "sample_width": self.sample_width, + "fovy": self.fovy, + "proj_mtx": self.proj_mtx, + } + + +@register("eff-random-camera-datamodule") +class EffRandomCameraDataModule(pl.LightningDataModule): + cfg: EffRandomCameraDataModuleConfig + + def __init__(self, cfg: Optional[Union[dict, DictConfig]] = None) -> None: + super().__init__() + self.cfg = parse_structured(EffRandomCameraDataModuleConfig, cfg) + + def setup(self, stage=None) -> None: + if stage in [None, "fit"]: + self.train_dataset = EffRandomCameraIterableDataset(self.cfg) + if stage in [None, "fit", "validate"]: + self.val_dataset = RandomCameraDataset(self.cfg, "val") + if stage in [None, "test", "predict"]: + self.test_dataset = RandomCameraDataset(self.cfg, "test") + + def prepare_data(self): + pass + + def general_loader(self, dataset, batch_size, collate_fn=None) -> DataLoader: + return DataLoader( + dataset, + # very important to disable multi-processing if you want to change self attributes at runtime! + # (for example setting self.width and self.height in update_step) + num_workers=5, # type: ignore + batch_size=batch_size, + collate_fn=collate_fn, + ) + + def train_dataloader(self) -> DataLoader: + return self.general_loader( + self.train_dataset, batch_size=None, collate_fn=self.train_dataset.collate + ) + + def val_dataloader(self) -> DataLoader: + return self.general_loader( + self.val_dataset, batch_size=1, collate_fn=self.val_dataset.collate + ) + # return self.general_loader(self.train_dataset, batch_size=None, collate_fn=self.train_dataset.collate) + + def test_dataloader(self) -> DataLoader: + return self.general_loader( + self.test_dataset, batch_size=1, collate_fn=self.test_dataset.collate + ) + + def predict_dataloader(self) -> DataLoader: + return self.general_loader( + self.test_dataset, batch_size=1, collate_fn=self.test_dataset.collate + ) diff --git a/threestudio/models/__init__.py b/threestudio/models/__init__.py new file mode 100644 index 0000000..9738918 --- /dev/null +++ b/threestudio/models/__init__.py @@ -0,0 +1,9 @@ +from . import ( + background, + exporters, + geometry, + guidance, + materials, + prompt_processors, + renderers, +) diff --git a/threestudio/models/background/__init__.py b/threestudio/models/background/__init__.py new file mode 100644 index 0000000..c637e6b --- /dev/null +++ b/threestudio/models/background/__init__.py @@ -0,0 +1,6 @@ +from . import ( + base, + neural_environment_map_background, + solid_color_background, + textured_background, +) diff --git a/threestudio/models/background/base.py b/threestudio/models/background/base.py new file mode 100644 index 0000000..8911bd1 --- /dev/null +++ b/threestudio/models/background/base.py @@ -0,0 +1,24 @@ +import random +from dataclasses import dataclass, field + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.utils.base import BaseModule +from threestudio.utils.typing import * + + +class BaseBackground(BaseModule): + @dataclass + class Config(BaseModule.Config): + pass + + cfg: Config + + def configure(self): + pass + + def forward(self, dirs: Float[Tensor, "B H W 3"]) -> Float[Tensor, "B H W Nc"]: + raise NotImplementedError diff --git a/threestudio/models/background/neural_environment_map_background.py b/threestudio/models/background/neural_environment_map_background.py new file mode 100644 index 0000000..33adfc0 --- /dev/null +++ b/threestudio/models/background/neural_environment_map_background.py @@ -0,0 +1,67 @@ +import random +from dataclasses import dataclass, field + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.models.background.base import BaseBackground +from threestudio.models.networks import get_encoding, get_mlp +from threestudio.utils.ops import get_activation +from threestudio.utils.typing import * + + +@threestudio.register("neural-environment-map-background") +class NeuralEnvironmentMapBackground(BaseBackground): + @dataclass + class Config(BaseBackground.Config): + n_output_dims: int = 3 + color_activation: str = "sigmoid" + dir_encoding_config: dict = field( + default_factory=lambda: {"otype": "SphericalHarmonics", "degree": 3} + ) + mlp_network_config: dict = field( + default_factory=lambda: { + "otype": "VanillaMLP", + "activation": "ReLU", + "n_neurons": 16, + "n_hidden_layers": 2, + } + ) + random_aug: bool = False + random_aug_prob: float = 0.5 + eval_color: Optional[Tuple[float, float, float]] = None + + cfg: Config + + def configure(self) -> None: + self.encoding = get_encoding(3, self.cfg.dir_encoding_config) + self.network = get_mlp( + self.encoding.n_output_dims, + self.cfg.n_output_dims, + self.cfg.mlp_network_config, + ) + + def forward(self, dirs: Float[Tensor, "B H W 3"]) -> Float[Tensor, "B H W Nc"]: + if not self.training and self.cfg.eval_color is not None: + return torch.ones(*dirs.shape[:-1], self.cfg.n_output_dims).to( + dirs + ) * torch.as_tensor(self.cfg.eval_color).to(dirs) + # viewdirs must be normalized before passing to this function + dirs = (dirs + 1.0) / 2.0 # (-1, 1) => (0, 1) + dirs_embd = self.encoding(dirs.view(-1, 3)) + color = self.network(dirs_embd).view(*dirs.shape[:-1], self.cfg.n_output_dims) + color = get_activation(self.cfg.color_activation)(color) + if ( + self.training + and self.cfg.random_aug + and random.random() < self.cfg.random_aug_prob + ): + # use random background color with probability random_aug_prob + color = color * 0 + ( # prevent checking for unused parameters in DDP + torch.rand(dirs.shape[0], 1, 1, self.cfg.n_output_dims) + .to(dirs) + .expand(*dirs.shape[:-1], -1) + ) + return color diff --git a/threestudio/models/background/solid_color_background.py b/threestudio/models/background/solid_color_background.py new file mode 100644 index 0000000..0b68d5b --- /dev/null +++ b/threestudio/models/background/solid_color_background.py @@ -0,0 +1,51 @@ +import random +from dataclasses import dataclass, field + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.models.background.base import BaseBackground +from threestudio.utils.typing import * + + +@threestudio.register("solid-color-background") +class SolidColorBackground(BaseBackground): + @dataclass + class Config(BaseBackground.Config): + n_output_dims: int = 3 + color: Tuple = (1.0, 1.0, 1.0) + learned: bool = False + random_aug: bool = False + random_aug_prob: float = 0.5 + + cfg: Config + + def configure(self) -> None: + self.env_color: Float[Tensor, "Nc"] + if self.cfg.learned: + self.env_color = nn.Parameter( + torch.as_tensor(self.cfg.color, dtype=torch.float32) + ) + else: + self.register_buffer( + "env_color", torch.as_tensor(self.cfg.color, dtype=torch.float32) + ) + + def forward(self, dirs: Float[Tensor, "B H W 3"]) -> Float[Tensor, "B H W Nc"]: + color = torch.ones(*dirs.shape[:-1], self.cfg.n_output_dims).to( + dirs + ) * self.env_color.to(dirs) + if ( + self.training + and self.cfg.random_aug + and random.random() < self.cfg.random_aug_prob + ): + # use random background color with probability random_aug_prob + color = color * 0 + ( # prevent checking for unused parameters in DDP + torch.rand(dirs.shape[0], 1, 1, self.cfg.n_output_dims) + .to(dirs) + .expand(*dirs.shape[:-1], -1) + ) + return color diff --git a/threestudio/models/background/textured_background.py b/threestudio/models/background/textured_background.py new file mode 100644 index 0000000..ee0a0ca --- /dev/null +++ b/threestudio/models/background/textured_background.py @@ -0,0 +1,54 @@ +from dataclasses import dataclass, field + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.models.background.base import BaseBackground +from threestudio.utils.ops import get_activation +from threestudio.utils.typing import * + + +@threestudio.register("textured-background") +class TexturedBackground(BaseBackground): + @dataclass + class Config(BaseBackground.Config): + n_output_dims: int = 3 + height: int = 64 + width: int = 64 + color_activation: str = "sigmoid" + + cfg: Config + + def configure(self) -> None: + self.texture = nn.Parameter( + torch.randn((1, self.cfg.n_output_dims, self.cfg.height, self.cfg.width)) + ) + + def spherical_xyz_to_uv(self, dirs: Float[Tensor, "*B 3"]) -> Float[Tensor, "*B 2"]: + x, y, z = dirs[..., 0], dirs[..., 1], dirs[..., 2] + xy = (x**2 + y**2) ** 0.5 + u = torch.atan2(xy, z) / torch.pi + v = torch.atan2(y, x) / (torch.pi * 2) + 0.5 + uv = torch.stack([u, v], -1) + return uv + + def forward(self, dirs: Float[Tensor, "*B 3"]) -> Float[Tensor, "*B Nc"]: + dirs_shape = dirs.shape[:-1] + uv = self.spherical_xyz_to_uv(dirs.reshape(-1, dirs.shape[-1])) + uv = 2 * uv - 1 # rescale to [-1, 1] for grid_sample + uv = uv.reshape(1, -1, 1, 2) + color = ( + F.grid_sample( + self.texture, + uv, + mode="bilinear", + padding_mode="reflection", + align_corners=False, + ) + .reshape(self.cfg.n_output_dims, -1) + .T.reshape(*dirs_shape, self.cfg.n_output_dims) + ) + color = get_activation(self.cfg.color_activation)(color) + return color diff --git a/threestudio/models/estimators.py b/threestudio/models/estimators.py new file mode 100644 index 0000000..c4c6c5e --- /dev/null +++ b/threestudio/models/estimators.py @@ -0,0 +1,118 @@ +from typing import Callable, List, Optional, Tuple + +try: + from typing import Literal +except ImportError: + from typing_extensions import Literal + +import torch +from nerfacc.data_specs import RayIntervals +from nerfacc.estimators.base import AbstractEstimator +from nerfacc.pdf import importance_sampling, searchsorted +from nerfacc.volrend import render_transmittance_from_density +from torch import Tensor + + +class ImportanceEstimator(AbstractEstimator): + def __init__( + self, + ) -> None: + super().__init__() + + @torch.no_grad() + def sampling( + self, + prop_sigma_fns: List[Callable], + prop_samples: List[int], + num_samples: int, + # rendering options + n_rays: int, + near_plane: float, + far_plane: float, + sampling_type: Literal["uniform", "lindisp"] = "uniform", + # training options + stratified: bool = False, + requires_grad: bool = False, + ) -> Tuple[Tensor, Tensor]: + """Sampling with CDFs from proposal networks. + + Args: + prop_sigma_fns: Proposal network evaluate functions. It should be a list + of functions that take in samples {t_starts (n_rays, n_samples), + t_ends (n_rays, n_samples)} and returns the post-activation densities + (n_rays, n_samples). + prop_samples: Number of samples to draw from each proposal network. Should + be the same length as `prop_sigma_fns`. + num_samples: Number of samples to draw in the end. + n_rays: Number of rays. + near_plane: Near plane. + far_plane: Far plane. + sampling_type: Sampling type. Either "uniform" or "lindisp". Default to + "lindisp". + stratified: Whether to use stratified sampling. Default to `False`. + + Returns: + A tuple of {Tensor, Tensor}: + + - **t_starts**: The starts of the samples. Shape (n_rays, num_samples). + - **t_ends**: The ends of the samples. Shape (n_rays, num_samples). + + """ + assert len(prop_sigma_fns) == len(prop_samples), ( + "The number of proposal networks and the number of samples " + "should be the same." + ) + cdfs = torch.cat( + [ + torch.zeros((n_rays, 1), device=self.device), + torch.ones((n_rays, 1), device=self.device), + ], + dim=-1, + ) + intervals = RayIntervals(vals=cdfs) + + for level_fn, level_samples in zip(prop_sigma_fns, prop_samples): + intervals, _ = importance_sampling( + intervals, cdfs, level_samples, stratified + ) + t_vals = _transform_stot( + sampling_type, intervals.vals, near_plane, far_plane + ) + t_starts = t_vals[..., :-1] + t_ends = t_vals[..., 1:] + + with torch.set_grad_enabled(requires_grad): + sigmas = level_fn(t_starts, t_ends) + assert sigmas.shape == t_starts.shape + trans, _ = render_transmittance_from_density(t_starts, t_ends, sigmas) + cdfs = 1.0 - torch.cat([trans, torch.zeros_like(trans[:, :1])], dim=-1) + + intervals, _ = importance_sampling(intervals, cdfs, num_samples, stratified) + t_vals_fine = _transform_stot( + sampling_type, intervals.vals, near_plane, far_plane + ) + + t_vals = torch.cat([t_vals, t_vals_fine], dim=-1) + t_vals, _ = torch.sort(t_vals, dim=-1) + + t_starts_ = t_vals[..., :-1] + t_ends_ = t_vals[..., 1:] + + return t_starts_, t_ends_ + + +def _transform_stot( + transform_type: Literal["uniform", "lindisp"], + s_vals: torch.Tensor, + t_min: torch.Tensor, + t_max: torch.Tensor, +) -> torch.Tensor: + if transform_type == "uniform": + _contract_fn, _icontract_fn = lambda x: x, lambda x: x + elif transform_type == "lindisp": + _contract_fn, _icontract_fn = lambda x: 1 / x, lambda x: 1 / x + else: + raise ValueError(f"Unknown transform_type: {transform_type}") + s_min, s_max = _contract_fn(t_min), _contract_fn(t_max) + icontract_fn = lambda s: _icontract_fn(s * s_max + (1 - s) * s_min) + return icontract_fn(s_vals) diff --git a/threestudio/models/exporters/__init__.py b/threestudio/models/exporters/__init__.py new file mode 100644 index 0000000..add385e --- /dev/null +++ b/threestudio/models/exporters/__init__.py @@ -0,0 +1 @@ +from . import base, mesh_exporter diff --git a/threestudio/models/exporters/base.py b/threestudio/models/exporters/base.py new file mode 100644 index 0000000..592d103 --- /dev/null +++ b/threestudio/models/exporters/base.py @@ -0,0 +1,59 @@ +from dataclasses import dataclass + +import threestudio +from threestudio.models.background.base import BaseBackground +from threestudio.models.geometry.base import BaseImplicitGeometry +from threestudio.models.materials.base import BaseMaterial +from threestudio.utils.base import BaseObject +from threestudio.utils.typing import * + + +@dataclass +class ExporterOutput: + save_name: str + save_type: str + params: Dict[str, Any] + + +class Exporter(BaseObject): + @dataclass + class Config(BaseObject.Config): + save_video: bool = False + + cfg: Config + + def configure( + self, + geometry: BaseImplicitGeometry, + material: BaseMaterial, + background: BaseBackground, + ) -> None: + @dataclass + class SubModules: + geometry: BaseImplicitGeometry + material: BaseMaterial + background: BaseBackground + + self.sub_modules = SubModules(geometry, material, background) + + @property + def geometry(self) -> BaseImplicitGeometry: + return self.sub_modules.geometry + + @property + def material(self) -> BaseMaterial: + return self.sub_modules.material + + @property + def background(self) -> BaseBackground: + return self.sub_modules.background + + def __call__(self, *args, **kwargs) -> List[ExporterOutput]: + raise NotImplementedError + + +@threestudio.register("dummy-exporter") +class DummyExporter(Exporter): + def __call__(self, *args, **kwargs) -> List[ExporterOutput]: + # DummyExporter does not export anything + return [] diff --git a/threestudio/models/exporters/mesh_exporter.py b/threestudio/models/exporters/mesh_exporter.py new file mode 100644 index 0000000..2c67826 --- /dev/null +++ b/threestudio/models/exporters/mesh_exporter.py @@ -0,0 +1,175 @@ +from dataclasses import dataclass, field + +import cv2 +import numpy as np +import torch + +import threestudio +from threestudio.models.background.base import BaseBackground +from threestudio.models.exporters.base import Exporter, ExporterOutput +from threestudio.models.geometry.base import BaseImplicitGeometry +from threestudio.models.materials.base import BaseMaterial +from threestudio.models.mesh import Mesh +from threestudio.utils.rasterize import NVDiffRasterizerContext +from threestudio.utils.typing import * + + +@threestudio.register("mesh-exporter") +class MeshExporter(Exporter): + @dataclass + class Config(Exporter.Config): + fmt: str = "obj-mtl" # in ['obj-mtl', 'obj'], TODO: fbx + save_name: str = "model" + save_normal: bool = False + save_uv: bool = True + save_texture: bool = True + texture_size: int = 1024 + texture_format: str = "jpg" + xatlas_chart_options: dict = field(default_factory=dict) + xatlas_pack_options: dict = field(default_factory=dict) + context_type: str = "gl" + + cfg: Config + + def configure( + self, + geometry: BaseImplicitGeometry, + material: BaseMaterial, + background: BaseBackground, + ) -> None: + super().configure(geometry, material, background) + self.ctx = NVDiffRasterizerContext(self.cfg.context_type, self.device) + + def __call__(self) -> List[ExporterOutput]: + mesh: Mesh = self.geometry.isosurface() + + if self.cfg.fmt == "obj-mtl": + return self.export_obj_with_mtl(mesh) + elif self.cfg.fmt == "obj": + return self.export_obj(mesh) + else: + raise ValueError(f"Unsupported mesh export format: {self.cfg.fmt}") + + def export_obj_with_mtl(self, mesh: Mesh) -> List[ExporterOutput]: + params = { + "mesh": mesh, + "save_mat": True, + "save_normal": self.cfg.save_normal, + "save_uv": self.cfg.save_uv, + "save_vertex_color": False, + "map_Kd": None, # Base Color + "map_Ks": None, # Specular + "map_Bump": None, # Normal + # ref: https://en.wikipedia.org/wiki/Wavefront_.obj_file#Physically-based_Rendering + "map_Pm": None, # Metallic + "map_Pr": None, # Roughness + "map_format": self.cfg.texture_format, + } + + if self.cfg.save_uv: + mesh.unwrap_uv(self.cfg.xatlas_chart_options, self.cfg.xatlas_pack_options) + + if self.cfg.save_texture: + threestudio.info("Exporting textures ...") + assert self.cfg.save_uv, "save_uv must be True when save_texture is True" + # clip space transform + uv_clip = mesh.v_tex * 2.0 - 1.0 + # pad to four component coordinate + uv_clip4 = torch.cat( + ( + uv_clip, + torch.zeros_like(uv_clip[..., 0:1]), + torch.ones_like(uv_clip[..., 0:1]), + ), + dim=-1, + ) + # rasterize + rast, _ = self.ctx.rasterize_one( + uv_clip4, mesh.t_tex_idx, (self.cfg.texture_size, self.cfg.texture_size) + ) + + hole_mask = ~(rast[:, :, 3] > 0) + + def uv_padding(image): + uv_padding_size = self.cfg.xatlas_pack_options.get("padding", 2) + inpaint_image = ( + cv2.inpaint( + (image.detach().cpu().numpy() * 255).astype(np.uint8), + (hole_mask.detach().cpu().numpy() * 255).astype(np.uint8), + uv_padding_size, + cv2.INPAINT_TELEA, + ) + / 255.0 + ) + return torch.from_numpy(inpaint_image).to(image) + + # Interpolate world space position + gb_pos, _ = self.ctx.interpolate_one( + mesh.v_pos, rast[None, ...], mesh.t_pos_idx + ) + gb_pos = gb_pos[0] + + # Sample out textures from MLP + geo_out = self.geometry.export(points=gb_pos) + mat_out = self.material.export(points=gb_pos, **geo_out) + + threestudio.info( + "Perform UV padding on texture maps to avoid seams, may take a while ..." + ) + + if "albedo" in mat_out: + params["map_Kd"] = uv_padding(mat_out["albedo"]) + else: + threestudio.warn( + "save_texture is True but no albedo texture found, using default white texture" + ) + if "metallic" in mat_out: + params["map_Pm"] = uv_padding(mat_out["metallic"]) + if "roughness" in mat_out: + params["map_Pr"] = uv_padding(mat_out["roughness"]) + if "bump" in mat_out: + params["map_Bump"] = uv_padding(mat_out["bump"]) + # TODO: map_Ks + return [ + ExporterOutput( + save_name=f"{self.cfg.save_name}.obj", save_type="obj", params=params + ) + ] + + def export_obj(self, mesh: Mesh) -> List[ExporterOutput]: + params = { + "mesh": mesh, + "save_mat": False, + "save_normal": self.cfg.save_normal, + "save_uv": self.cfg.save_uv, + "save_vertex_color": False, + "map_Kd": None, # Base Color + "map_Ks": None, # Specular + "map_Bump": None, # Normal + # ref: https://en.wikipedia.org/wiki/Wavefront_.obj_file#Physically-based_Rendering + "map_Pm": None, # Metallic + "map_Pr": None, # Roughness + "map_format": self.cfg.texture_format, + } + + if self.cfg.save_uv: + mesh.unwrap_uv(self.cfg.xatlas_chart_options, self.cfg.xatlas_pack_options) + + if self.cfg.save_texture: + threestudio.info("Exporting textures ...") + geo_out = self.geometry.export(points=mesh.v_pos) + mat_out = self.material.export(points=mesh.v_pos, **geo_out) + + if "albedo" in mat_out: + mesh.set_vertex_color(mat_out["albedo"]) + params["save_vertex_color"] = True + else: + threestudio.warn( + "save_texture is True but no albedo texture found, not saving vertex color" + ) + + return [ + ExporterOutput( + save_name=f"{self.cfg.save_name}.obj", save_type="obj", params=params + ) + ] diff --git a/threestudio/models/geometry/__init__.py b/threestudio/models/geometry/__init__.py new file mode 100644 index 0000000..19185d2 --- /dev/null +++ b/threestudio/models/geometry/__init__.py @@ -0,0 +1,8 @@ +from . import ( + base, + custom_mesh, + implicit_sdf, + implicit_volume, + tetrahedra_sdf_grid, + volume_grid, +) diff --git a/threestudio/models/geometry/base.py b/threestudio/models/geometry/base.py new file mode 100644 index 0000000..8190fb1 --- /dev/null +++ b/threestudio/models/geometry/base.py @@ -0,0 +1,209 @@ +from dataclasses import dataclass, field + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.models.isosurface import ( + IsosurfaceHelper, + MarchingCubeCPUHelper, + MarchingTetrahedraHelper, +) +from threestudio.models.mesh import Mesh +from threestudio.utils.base import BaseModule +from threestudio.utils.ops import chunk_batch, scale_tensor +from threestudio.utils.typing import * + + +def contract_to_unisphere( + x: Float[Tensor, "... 3"], bbox: Float[Tensor, "2 3"], unbounded: bool = False +) -> Float[Tensor, "... 3"]: + if unbounded: + x = scale_tensor(x, bbox, (0, 1)) + x = x * 2 - 1 # aabb is at [-1, 1] + mag = x.norm(dim=-1, keepdim=True) + mask = mag.squeeze(-1) > 1 + x[mask] = (2 - 1 / mag[mask]) * (x[mask] / mag[mask]) + x = x / 4 + 0.5 # [-inf, inf] is at [0, 1] + else: + x = scale_tensor(x, bbox, (0, 1)) + return x + + +class BaseGeometry(BaseModule): + @dataclass + class Config(BaseModule.Config): + pass + + cfg: Config + + @staticmethod + def create_from( + other: "BaseGeometry", cfg: Optional[Union[dict, DictConfig]] = None, **kwargs + ) -> "BaseGeometry": + raise TypeError( + f"Cannot create {BaseGeometry.__name__} from {other.__class__.__name__}" + ) + + def export(self, *args, **kwargs) -> Dict[str, Any]: + return {} + + +class BaseImplicitGeometry(BaseGeometry): + @dataclass + class Config(BaseGeometry.Config): + radius: float = 1.0 + isosurface: bool = True + isosurface_method: str = "mt" + isosurface_resolution: int = 128 + isosurface_threshold: Union[float, str] = 0.0 + isosurface_chunk: int = 0 + isosurface_coarse_to_fine: bool = True + isosurface_deformable_grid: bool = False + isosurface_remove_outliers: bool = True + isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01 + + cfg: Config + + def configure(self) -> None: + self.bbox: Float[Tensor, "2 3"] + self.register_buffer( + "bbox", + torch.as_tensor( + [ + [-self.cfg.radius, -self.cfg.radius, -self.cfg.radius], + [self.cfg.radius, self.cfg.radius, self.cfg.radius], + ], + dtype=torch.float32, + ), + ) + self.isosurface_helper: Optional[IsosurfaceHelper] = None + self.unbounded: bool = False + + def _initilize_isosurface_helper(self): + if self.cfg.isosurface and self.isosurface_helper is None: + if self.cfg.isosurface_method == "mc-cpu": + self.isosurface_helper = MarchingCubeCPUHelper( + self.cfg.isosurface_resolution + ).to(self.device) + elif self.cfg.isosurface_method == "mt": + self.isosurface_helper = MarchingTetrahedraHelper( + self.cfg.isosurface_resolution, + f"load/tets/{self.cfg.isosurface_resolution}_tets.npz", + ).to(self.device) + else: + raise AttributeError( + "Unknown isosurface method {self.cfg.isosurface_method}" + ) + + def forward( + self, points: Float[Tensor, "*N Di"], output_normal: bool = False + ) -> Dict[str, Float[Tensor, "..."]]: + raise NotImplementedError + + def forward_field( + self, points: Float[Tensor, "*N Di"] + ) -> Tuple[Float[Tensor, "*N 1"], Optional[Float[Tensor, "*N 3"]]]: + # return the value of the implicit field, could be density / signed distance + # also return a deformation field if the grid vertices can be optimized + raise NotImplementedError + + def forward_level( + self, field: Float[Tensor, "*N 1"], threshold: float + ) -> Float[Tensor, "*N 1"]: + # return the value of the implicit field, where the zero level set represents the surface + raise NotImplementedError + + def _isosurface(self, bbox: Float[Tensor, "2 3"], fine_stage: bool = False) -> Mesh: + def batch_func(x): + # scale to bbox as the input vertices are in [0, 1] + field, deformation = self.forward_field( + scale_tensor( + x.to(bbox.device), self.isosurface_helper.points_range, bbox + ), + ) + field = field.to( + x.device + ) # move to the same device as the input (could be CPU) + if deformation is not None: + deformation = deformation.to(x.device) + return field, deformation + + assert self.isosurface_helper is not None + + field, deformation = chunk_batch( + batch_func, + self.cfg.isosurface_chunk, + self.isosurface_helper.grid_vertices, + ) + + threshold: float + + if isinstance(self.cfg.isosurface_threshold, float): + threshold = self.cfg.isosurface_threshold + elif self.cfg.isosurface_threshold == "auto": + eps = 1.0e-5 + threshold = field[field > eps].mean().item() + threestudio.info( + f"Automatically determined isosurface threshold: {threshold}" + ) + else: + raise TypeError( + f"Unknown isosurface_threshold {self.cfg.isosurface_threshold}" + ) + + level = self.forward_level(field, threshold) + mesh: Mesh = self.isosurface_helper(level, deformation=deformation) + mesh.v_pos = scale_tensor( + mesh.v_pos, self.isosurface_helper.points_range, bbox + ) # scale to bbox as the grid vertices are in [0, 1] + mesh.add_extra("bbox", bbox) + + if self.cfg.isosurface_remove_outliers: + # remove outliers components with small number of faces + # only enabled when the mesh is not differentiable + mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold) + + return mesh + + def isosurface(self) -> Mesh: + if not self.cfg.isosurface: + raise NotImplementedError( + "Isosurface is not enabled in the current configuration" + ) + self._initilize_isosurface_helper() + if self.cfg.isosurface_coarse_to_fine: + threestudio.debug("First run isosurface to get a tight bounding box ...") + with torch.no_grad(): + mesh_coarse = self._isosurface(self.bbox) + vmin, vmax = mesh_coarse.v_pos.amin(dim=0), mesh_coarse.v_pos.amax(dim=0) + vmin_ = (vmin - (vmax - vmin) * 0.1).max(self.bbox[0]) + vmax_ = (vmax + (vmax - vmin) * 0.1).min(self.bbox[1]) + threestudio.debug("Run isosurface again with the tight bounding box ...") + mesh = self._isosurface(torch.stack([vmin_, vmax_], dim=0), fine_stage=True) + else: + mesh = self._isosurface(self.bbox) + return mesh + + +class BaseExplicitGeometry(BaseGeometry): + @dataclass + class Config(BaseGeometry.Config): + radius: float = 1.0 + + cfg: Config + + def configure(self) -> None: + self.bbox: Float[Tensor, "2 3"] + self.register_buffer( + "bbox", + torch.as_tensor( + [ + [-self.cfg.radius, -self.cfg.radius, -self.cfg.radius], + [self.cfg.radius, self.cfg.radius, self.cfg.radius], + ], + dtype=torch.float32, + ), + ) diff --git a/threestudio/models/geometry/custom_mesh.py b/threestudio/models/geometry/custom_mesh.py new file mode 100644 index 0000000..08ebc63 --- /dev/null +++ b/threestudio/models/geometry/custom_mesh.py @@ -0,0 +1,178 @@ +import os +from dataclasses import dataclass, field + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.models.geometry.base import ( + BaseExplicitGeometry, + BaseGeometry, + contract_to_unisphere, +) +from threestudio.models.mesh import Mesh +from threestudio.models.networks import get_encoding, get_mlp +from threestudio.utils.ops import scale_tensor +from threestudio.utils.typing import * + + +@threestudio.register("custom-mesh") +class CustomMesh(BaseExplicitGeometry): + @dataclass + class Config(BaseExplicitGeometry.Config): + n_input_dims: int = 3 + n_feature_dims: int = 3 + pos_encoding_config: dict = field( + default_factory=lambda: { + "otype": "HashGrid", + "n_levels": 16, + "n_features_per_level": 2, + "log2_hashmap_size": 19, + "base_resolution": 16, + "per_level_scale": 1.447269237440378, + } + ) + mlp_network_config: dict = field( + default_factory=lambda: { + "otype": "VanillaMLP", + "activation": "ReLU", + "output_activation": "none", + "n_neurons": 64, + "n_hidden_layers": 1, + } + ) + shape_init: str = "" + shape_init_params: Optional[Any] = None + shape_init_mesh_up: str = "+z" + shape_init_mesh_front: str = "+x" + + cfg: Config + + def configure(self) -> None: + super().configure() + + self.encoding = get_encoding( + self.cfg.n_input_dims, self.cfg.pos_encoding_config + ) + self.feature_network = get_mlp( + self.encoding.n_output_dims, + self.cfg.n_feature_dims, + self.cfg.mlp_network_config, + ) + + # Initialize custom mesh + if self.cfg.shape_init.startswith("mesh:"): + assert isinstance(self.cfg.shape_init_params, float) + mesh_path = self.cfg.shape_init[5:] + if not os.path.exists(mesh_path): + raise ValueError(f"Mesh file {mesh_path} does not exist.") + + import trimesh + + scene = trimesh.load(mesh_path) + if isinstance(scene, trimesh.Trimesh): + mesh = scene + elif isinstance(scene, trimesh.scene.Scene): + mesh = trimesh.Trimesh() + for obj in scene.geometry.values(): + mesh = trimesh.util.concatenate([mesh, obj]) + else: + raise ValueError(f"Unknown mesh type at {mesh_path}.") + + # move to center + centroid = mesh.vertices.mean(0) + mesh.vertices = mesh.vertices - centroid + + # align to up-z and front-x + dirs = ["+x", "+y", "+z", "-x", "-y", "-z"] + dir2vec = { + "+x": np.array([1, 0, 0]), + "+y": np.array([0, 1, 0]), + "+z": np.array([0, 0, 1]), + "-x": np.array([-1, 0, 0]), + "-y": np.array([0, -1, 0]), + "-z": np.array([0, 0, -1]), + } + if ( + self.cfg.shape_init_mesh_up not in dirs + or self.cfg.shape_init_mesh_front not in dirs + ): + raise ValueError( + f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}." + ) + if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]: + raise ValueError( + "shape_init_mesh_up and shape_init_mesh_front must be orthogonal." + ) + z_, x_ = ( + dir2vec[self.cfg.shape_init_mesh_up], + dir2vec[self.cfg.shape_init_mesh_front], + ) + y_ = np.cross(z_, x_) + std2mesh = np.stack([x_, y_, z_], axis=0).T + mesh2std = np.linalg.inv(std2mesh) + + # scaling + scale = np.abs(mesh.vertices).max() + mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params + mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T + + v_pos = torch.tensor(mesh.vertices, dtype=torch.float32).to(self.device) + t_pos_idx = torch.tensor(mesh.faces, dtype=torch.int64).to(self.device) + self.mesh = Mesh(v_pos=v_pos, t_pos_idx=t_pos_idx) + self.register_buffer( + "v_buffer", + v_pos, + ) + self.register_buffer( + "t_buffer", + t_pos_idx, + ) + + else: + raise ValueError( + f"Unknown shape initialization type: {self.cfg.shape_init}" + ) + print(self.mesh.v_pos.device) + + def isosurface(self) -> Mesh: + if hasattr(self, "mesh"): + return self.mesh + elif hasattr(self, "v_buffer"): + self.mesh = Mesh(v_pos=self.v_buffer, t_pos_idx=self.t_buffer) + return self.mesh + else: + raise ValueError(f"custom mesh is not initialized") + + def forward( + self, points: Float[Tensor, "*N Di"], output_normal: bool = False + ) -> Dict[str, Float[Tensor, "..."]]: + assert ( + output_normal == False + ), f"Normal output is not supported for {self.__class__.__name__}" + points_unscaled = points # points in the original scale + points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1) + enc = self.encoding(points.view(-1, self.cfg.n_input_dims)) + features = self.feature_network(enc).view( + *points.shape[:-1], self.cfg.n_feature_dims + ) + return {"features": features} + + def export(self, points: Float[Tensor, "*N Di"], **kwargs) -> Dict[str, Any]: + out: Dict[str, Any] = {} + if self.cfg.n_feature_dims == 0: + return out + points_unscaled = points + points = contract_to_unisphere(points_unscaled, self.bbox) + enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims)) + features = self.feature_network(enc).view( + *points.shape[:-1], self.cfg.n_feature_dims + ) + out.update( + { + "features": features, + } + ) + return out diff --git a/threestudio/models/geometry/implicit_sdf.py b/threestudio/models/geometry/implicit_sdf.py new file mode 100644 index 0000000..9589291 --- /dev/null +++ b/threestudio/models/geometry/implicit_sdf.py @@ -0,0 +1,413 @@ +import os +from dataclasses import dataclass, field + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.models.geometry.base import BaseImplicitGeometry, contract_to_unisphere +from threestudio.models.mesh import Mesh +from threestudio.models.networks import get_encoding, get_mlp +from threestudio.utils.misc import broadcast, get_rank +from threestudio.utils.typing import * + + +@threestudio.register("implicit-sdf") +class ImplicitSDF(BaseImplicitGeometry): + @dataclass + class Config(BaseImplicitGeometry.Config): + n_input_dims: int = 3 + n_feature_dims: int = 3 + pos_encoding_config: dict = field( + default_factory=lambda: { + "otype": "HashGrid", + "n_levels": 16, + "n_features_per_level": 2, + "log2_hashmap_size": 19, + "base_resolution": 16, + "per_level_scale": 1.447269237440378, + } + ) + mlp_network_config: dict = field( + default_factory=lambda: { + "otype": "VanillaMLP", + "activation": "ReLU", + "output_activation": "none", + "n_neurons": 64, + "n_hidden_layers": 1, + } + ) + normal_type: Optional[ + str + ] = "finite_difference" # in ['pred', 'finite_difference', 'finite_difference_laplacian'] + finite_difference_normal_eps: Union[ + float, str + ] = 0.01 # in [float, "progressive"] + shape_init: Optional[str] = None + shape_init_params: Optional[Any] = None + shape_init_mesh_up: str = "+z" + shape_init_mesh_front: str = "+x" + force_shape_init: bool = False + sdf_bias: Union[float, str] = 0.0 + sdf_bias_params: Optional[Any] = None + + # no need to removal outlier for SDF + isosurface_remove_outliers: bool = False + + cfg: Config + + def configure(self) -> None: + super().configure() + self.encoding = get_encoding( + self.cfg.n_input_dims, self.cfg.pos_encoding_config + ) + self.sdf_network = get_mlp( + self.encoding.n_output_dims, 1, self.cfg.mlp_network_config + ) + + if self.cfg.n_feature_dims > 0: + self.feature_network = get_mlp( + self.encoding.n_output_dims, + self.cfg.n_feature_dims, + self.cfg.mlp_network_config, + ) + + if self.cfg.normal_type == "pred": + self.normal_network = get_mlp( + self.encoding.n_output_dims, 3, self.cfg.mlp_network_config + ) + if self.cfg.isosurface_deformable_grid: + assert ( + self.cfg.isosurface_method == "mt" + ), "isosurface_deformable_grid only works with mt" + self.deformation_network = get_mlp( + self.encoding.n_output_dims, 3, self.cfg.mlp_network_config + ) + + self.finite_difference_normal_eps: Optional[float] = None + + def initialize_shape(self) -> None: + if self.cfg.shape_init is None and not self.cfg.force_shape_init: + return + + # do not initialize shape if weights are provided + if self.cfg.weights is not None and not self.cfg.force_shape_init: + return + + if self.cfg.sdf_bias != 0.0: + threestudio.warn( + "shape_init and sdf_bias are both specified, which may lead to unexpected results." + ) + + get_gt_sdf: Callable[[Float[Tensor, "N 3"]], Float[Tensor, "N 1"]] + assert isinstance(self.cfg.shape_init, str) + if self.cfg.shape_init == "ellipsoid": + assert ( + isinstance(self.cfg.shape_init_params, Sized) + and len(self.cfg.shape_init_params) == 3 + ) + size = torch.as_tensor(self.cfg.shape_init_params).to(self.device) + + def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]: + return ((points_rand / size) ** 2).sum( + dim=-1, keepdim=True + ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid + + get_gt_sdf = func + elif self.cfg.shape_init == "sphere": + assert isinstance(self.cfg.shape_init_params, float) + radius = self.cfg.shape_init_params + + def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]: + return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius + + get_gt_sdf = func + elif self.cfg.shape_init.startswith("mesh:"): + assert isinstance(self.cfg.shape_init_params, float) + mesh_path = self.cfg.shape_init[5:] + if not os.path.exists(mesh_path): + raise ValueError(f"Mesh file {mesh_path} does not exist.") + + import trimesh + + scene = trimesh.load(mesh_path) + if isinstance(scene, trimesh.Trimesh): + mesh = scene + elif isinstance(scene, trimesh.scene.Scene): + mesh = trimesh.Trimesh() + for obj in scene.geometry.values(): + mesh = trimesh.util.concatenate([mesh, obj]) + else: + raise ValueError(f"Unknown mesh type at {mesh_path}.") + + # move to center + centroid = mesh.vertices.mean(0) + mesh.vertices = mesh.vertices - centroid + + # align to up-z and front-x + dirs = ["+x", "+y", "+z", "-x", "-y", "-z"] + dir2vec = { + "+x": np.array([1, 0, 0]), + "+y": np.array([0, 1, 0]), + "+z": np.array([0, 0, 1]), + "-x": np.array([-1, 0, 0]), + "-y": np.array([0, -1, 0]), + "-z": np.array([0, 0, -1]), + } + if ( + self.cfg.shape_init_mesh_up not in dirs + or self.cfg.shape_init_mesh_front not in dirs + ): + raise ValueError( + f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}." + ) + if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]: + raise ValueError( + "shape_init_mesh_up and shape_init_mesh_front must be orthogonal." + ) + z_, x_ = ( + dir2vec[self.cfg.shape_init_mesh_up], + dir2vec[self.cfg.shape_init_mesh_front], + ) + y_ = np.cross(z_, x_) + std2mesh = np.stack([x_, y_, z_], axis=0).T + mesh2std = np.linalg.inv(std2mesh) + + # scaling + scale = np.abs(mesh.vertices).max() + mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params + mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T + + from pysdf import SDF + + sdf = SDF(mesh.vertices, mesh.faces) + + def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]: + # add a negative signed here + # as in pysdf the inside of the shape has positive signed distance + return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to( + points_rand + )[..., None] + + get_gt_sdf = func + + else: + raise ValueError( + f"Unknown shape initialization type: {self.cfg.shape_init}" + ) + + # Initialize SDF to a given shape when no weights are provided or force_shape_init is True + optim = torch.optim.Adam(self.parameters(), lr=1e-3) + from tqdm import tqdm + + for _ in tqdm( + range(1000), + desc=f"Initializing SDF to a(n) {self.cfg.shape_init}:", + disable=get_rank() != 0, + ): + points_rand = ( + torch.rand((10000, 3), dtype=torch.float32).to(self.device) * 2.0 - 1.0 + ) + sdf_gt = get_gt_sdf(points_rand) + sdf_pred = self.forward_sdf(points_rand) + loss = F.mse_loss(sdf_pred, sdf_gt) + optim.zero_grad() + loss.backward() + optim.step() + + # explicit broadcast to ensure param consistency across ranks + for param in self.parameters(): + broadcast(param, src=0) + + def get_shifted_sdf( + self, points: Float[Tensor, "*N Di"], sdf: Float[Tensor, "*N 1"] + ) -> Float[Tensor, "*N 1"]: + sdf_bias: Union[float, Float[Tensor, "*N 1"]] + if self.cfg.sdf_bias == "ellipsoid": + assert ( + isinstance(self.cfg.sdf_bias_params, Sized) + and len(self.cfg.sdf_bias_params) == 3 + ) + size = torch.as_tensor(self.cfg.sdf_bias_params).to(points) + sdf_bias = ((points / size) ** 2).sum( + dim=-1, keepdim=True + ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid + elif self.cfg.sdf_bias == "sphere": + assert isinstance(self.cfg.sdf_bias_params, float) + radius = self.cfg.sdf_bias_params + sdf_bias = (points**2).sum(dim=-1, keepdim=True).sqrt() - radius + elif isinstance(self.cfg.sdf_bias, float): + sdf_bias = self.cfg.sdf_bias + else: + raise ValueError(f"Unknown sdf bias {self.cfg.sdf_bias}") + return sdf + sdf_bias + + def forward( + self, points: Float[Tensor, "*N Di"], output_normal: bool = False + ) -> Dict[str, Float[Tensor, "..."]]: + grad_enabled = torch.is_grad_enabled() + + if output_normal and self.cfg.normal_type == "analytic": + torch.set_grad_enabled(True) + points.requires_grad_(True) + + points_unscaled = points # points in the original scale + points = contract_to_unisphere( + points, self.bbox, self.unbounded + ) # points normalized to (0, 1) + + enc = self.encoding(points.view(-1, self.cfg.n_input_dims)) + sdf = self.sdf_network(enc).view(*points.shape[:-1], 1) + sdf = self.get_shifted_sdf(points_unscaled, sdf) + output = {"sdf": sdf} + + if self.cfg.n_feature_dims > 0: + features = self.feature_network(enc).view( + *points.shape[:-1], self.cfg.n_feature_dims + ) + output.update({"features": features}) + + if output_normal: + if ( + self.cfg.normal_type == "finite_difference" + or self.cfg.normal_type == "finite_difference_laplacian" + ): + assert self.finite_difference_normal_eps is not None + eps: float = self.finite_difference_normal_eps + if self.cfg.normal_type == "finite_difference_laplacian": + offsets: Float[Tensor, "6 3"] = torch.as_tensor( + [ + [eps, 0.0, 0.0], + [-eps, 0.0, 0.0], + [0.0, eps, 0.0], + [0.0, -eps, 0.0], + [0.0, 0.0, eps], + [0.0, 0.0, -eps], + ] + ).to(points_unscaled) + points_offset: Float[Tensor, "... 6 3"] = ( + points_unscaled[..., None, :] + offsets + ).clamp(-self.cfg.radius, self.cfg.radius) + sdf_offset: Float[Tensor, "... 6 1"] = self.forward_sdf( + points_offset + ) + sdf_grad = ( + 0.5 + * (sdf_offset[..., 0::2, 0] - sdf_offset[..., 1::2, 0]) + / eps + ) + else: + offsets: Float[Tensor, "3 3"] = torch.as_tensor( + [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]] + ).to(points_unscaled) + points_offset: Float[Tensor, "... 3 3"] = ( + points_unscaled[..., None, :] + offsets + ).clamp(-self.cfg.radius, self.cfg.radius) + sdf_offset: Float[Tensor, "... 3 1"] = self.forward_sdf( + points_offset + ) + sdf_grad = (sdf_offset[..., 0::1, 0] - sdf) / eps + normal = F.normalize(sdf_grad, dim=-1) + elif self.cfg.normal_type == "pred": + normal = self.normal_network(enc).view(*points.shape[:-1], 3) + normal = F.normalize(normal, dim=-1) + sdf_grad = normal + elif self.cfg.normal_type == "analytic": + sdf_grad = -torch.autograd.grad( + sdf, + points_unscaled, + grad_outputs=torch.ones_like(sdf), + create_graph=True, + )[0] + normal = F.normalize(sdf_grad, dim=-1) + if not grad_enabled: + sdf_grad = sdf_grad.detach() + normal = normal.detach() + else: + raise AttributeError(f"Unknown normal type {self.cfg.normal_type}") + output.update( + {"normal": normal, "shading_normal": normal, "sdf_grad": sdf_grad} + ) + return output + + def forward_sdf(self, points: Float[Tensor, "*N Di"]) -> Float[Tensor, "*N 1"]: + points_unscaled = points + points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded) + + sdf = self.sdf_network( + self.encoding(points.reshape(-1, self.cfg.n_input_dims)) + ).reshape(*points.shape[:-1], 1) + sdf = self.get_shifted_sdf(points_unscaled, sdf) + return sdf + + def forward_field( + self, points: Float[Tensor, "*N Di"] + ) -> Tuple[Float[Tensor, "*N 1"], Optional[Float[Tensor, "*N 3"]]]: + points_unscaled = points + points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded) + enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims)) + sdf = self.sdf_network(enc).reshape(*points.shape[:-1], 1) + sdf = self.get_shifted_sdf(points_unscaled, sdf) + deformation: Optional[Float[Tensor, "*N 3"]] = None + if self.cfg.isosurface_deformable_grid: + deformation = self.deformation_network(enc).reshape(*points.shape[:-1], 3) + return sdf, deformation + + def forward_level( + self, field: Float[Tensor, "*N 1"], threshold: float + ) -> Float[Tensor, "*N 1"]: + return field - threshold + + def export(self, points: Float[Tensor, "*N Di"], **kwargs) -> Dict[str, Any]: + out: Dict[str, Any] = {} + if self.cfg.n_feature_dims == 0: + return out + points_unscaled = points + points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded) + enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims)) + features = self.feature_network(enc).view( + *points.shape[:-1], self.cfg.n_feature_dims + ) + out.update( + { + "features": features, + } + ) + return out + + def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False): + if ( + self.cfg.normal_type == "finite_difference" + or self.cfg.normal_type == "finite_difference_laplacian" + ): + if isinstance(self.cfg.finite_difference_normal_eps, float): + self.finite_difference_normal_eps = ( + self.cfg.finite_difference_normal_eps + ) + elif self.cfg.finite_difference_normal_eps == "progressive": + # progressive finite difference eps from Neuralangelo + # https://arxiv.org/abs/2306.03092 + hg_conf: Any = self.cfg.pos_encoding_config + assert ( + hg_conf.otype == "ProgressiveBandHashGrid" + ), "finite_difference_normal_eps=progressive only works with ProgressiveBandHashGrid" + current_level = min( + hg_conf.start_level + + max(global_step - hg_conf.start_step, 0) // hg_conf.update_steps, + hg_conf.n_levels, + ) + grid_res = hg_conf.base_resolution * hg_conf.per_level_scale ** ( + current_level - 1 + ) + grid_size = 2 * self.cfg.radius / grid_res + if grid_size != self.finite_difference_normal_eps: + threestudio.info( + f"Update finite_difference_normal_eps to {grid_size}" + ) + self.finite_difference_normal_eps = grid_size + else: + raise ValueError( + f"Unknown finite_difference_normal_eps={self.cfg.finite_difference_normal_eps}" + ) diff --git a/threestudio/models/geometry/implicit_volume.py b/threestudio/models/geometry/implicit_volume.py new file mode 100644 index 0000000..cfee001 --- /dev/null +++ b/threestudio/models/geometry/implicit_volume.py @@ -0,0 +1,285 @@ +from dataclasses import dataclass, field + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.models.geometry.base import ( + BaseGeometry, + BaseImplicitGeometry, + contract_to_unisphere, +) +from threestudio.models.networks import get_encoding, get_mlp +from threestudio.utils.ops import get_activation +from threestudio.utils.typing import * + + +@threestudio.register("implicit-volume") +class ImplicitVolume(BaseImplicitGeometry): + @dataclass + class Config(BaseImplicitGeometry.Config): + n_input_dims: int = 3 + n_feature_dims: int = 3 + density_activation: Optional[str] = "softplus" + density_bias: Union[float, str] = "blob_magic3d" + density_blob_scale: float = 10.0 + density_blob_std: float = 0.5 + pos_encoding_config: dict = field( + default_factory=lambda: { + "otype": "HashGrid", + "n_levels": 16, + "n_features_per_level": 2, + "log2_hashmap_size": 19, + "base_resolution": 16, + "per_level_scale": 1.447269237440378, + } + ) + mlp_network_config: dict = field( + default_factory=lambda: { + "otype": "VanillaMLP", + "activation": "ReLU", + "output_activation": "none", + "n_neurons": 64, + "n_hidden_layers": 1, + } + ) + normal_type: Optional[ + str + ] = "finite_difference" # in ['pred', 'finite_difference', 'finite_difference_laplacian'] + finite_difference_normal_eps: float = 0.01 + + # automatically determine the threshold + isosurface_threshold: Union[float, str] = 25.0 + + # 4D Gaussian Annealing + anneal_density_blob_std_config: Optional[dict] = None + + cfg: Config + + def configure(self) -> None: + super().configure() + self.encoding = get_encoding( + self.cfg.n_input_dims, self.cfg.pos_encoding_config + ) + self.density_network = get_mlp( + self.encoding.n_output_dims, 1, self.cfg.mlp_network_config + ) + if self.cfg.n_feature_dims > 0: + self.feature_network = get_mlp( + self.encoding.n_output_dims, + self.cfg.n_feature_dims, + self.cfg.mlp_network_config, + ) + if self.cfg.normal_type == "pred": + self.normal_network = get_mlp( + self.encoding.n_output_dims, 3, self.cfg.mlp_network_config + ) + + def get_activated_density( + self, points: Float[Tensor, "*N Di"], density: Float[Tensor, "*N 1"] + ) -> Tuple[Float[Tensor, "*N 1"], Float[Tensor, "*N 1"]]: + density_bias: Union[float, Float[Tensor, "*N 1"]] + if self.cfg.density_bias == "blob_dreamfusion": + # pre-activation density bias + density_bias = ( + self.cfg.density_blob_scale + * torch.exp( + -0.5 * (points**2).sum(dim=-1) / self.cfg.density_blob_std**2 + )[..., None] + ) + elif self.cfg.density_bias == "blob_magic3d": + # pre-activation density bias + density_bias = ( + self.cfg.density_blob_scale + * ( + 1 + - torch.sqrt((points**2).sum(dim=-1)) / self.cfg.density_blob_std + )[..., None] + ) + elif isinstance(self.cfg.density_bias, float): + density_bias = self.cfg.density_bias + else: + raise ValueError(f"Unknown density bias {self.cfg.density_bias}") + raw_density: Float[Tensor, "*N 1"] = density + density_bias + density = get_activation(self.cfg.density_activation)(raw_density) + return raw_density, density + + def forward( + self, points: Float[Tensor, "*N Di"], output_normal: bool = False + ) -> Dict[str, Float[Tensor, "..."]]: + grad_enabled = torch.is_grad_enabled() + + if output_normal and self.cfg.normal_type == "analytic": + torch.set_grad_enabled(True) + points.requires_grad_(True) + + points_unscaled = points # points in the original scale + points = contract_to_unisphere( + points, self.bbox, self.unbounded + ) # points normalized to (0, 1) + + enc = self.encoding(points.view(-1, self.cfg.n_input_dims)) + density = self.density_network(enc).view(*points.shape[:-1], 1) + raw_density, density = self.get_activated_density(points_unscaled, density) + + output = { + "density": density, + } + + if self.cfg.n_feature_dims > 0: + features = self.feature_network(enc).view( + *points.shape[:-1], self.cfg.n_feature_dims + ) + output.update({"features": features}) + + if output_normal: + if ( + self.cfg.normal_type == "finite_difference" + or self.cfg.normal_type == "finite_difference_laplacian" + ): + # TODO: use raw density + eps = self.cfg.finite_difference_normal_eps + if self.cfg.normal_type == "finite_difference_laplacian": + offsets: Float[Tensor, "6 3"] = torch.as_tensor( + [ + [eps, 0.0, 0.0], + [-eps, 0.0, 0.0], + [0.0, eps, 0.0], + [0.0, -eps, 0.0], + [0.0, 0.0, eps], + [0.0, 0.0, -eps], + ] + ).to(points_unscaled) + points_offset: Float[Tensor, "... 6 3"] = ( + points_unscaled[..., None, :] + offsets + ).clamp(-self.cfg.radius, self.cfg.radius) + density_offset: Float[Tensor, "... 6 1"] = self.forward_density( + points_offset + ) + normal = ( + -0.5 + * (density_offset[..., 0::2, 0] - density_offset[..., 1::2, 0]) + / eps + ) + else: + offsets: Float[Tensor, "3 3"] = torch.as_tensor( + [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]] + ).to(points_unscaled) + points_offset: Float[Tensor, "... 3 3"] = ( + points_unscaled[..., None, :] + offsets + ).clamp(-self.cfg.radius, self.cfg.radius) + density_offset: Float[Tensor, "... 3 1"] = self.forward_density( + points_offset + ) + normal = -(density_offset[..., 0::1, 0] - density) / eps + normal = F.normalize(normal, dim=-1) + elif self.cfg.normal_type == "pred": + normal = self.normal_network(enc).view(*points.shape[:-1], 3) + normal = F.normalize(normal, dim=-1) + elif self.cfg.normal_type == "analytic": + normal = -torch.autograd.grad( + density, + points_unscaled, + grad_outputs=torch.ones_like(density), + create_graph=True, + )[0] + normal = F.normalize(normal, dim=-1) + if not grad_enabled: + normal = normal.detach() + else: + raise AttributeError(f"Unknown normal type {self.cfg.normal_type}") + output.update({"normal": normal, "shading_normal": normal}) + + torch.set_grad_enabled(grad_enabled) + return output + + def forward_density(self, points: Float[Tensor, "*N Di"]) -> Float[Tensor, "*N 1"]: + points_unscaled = points + points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded) + + density = self.density_network( + self.encoding(points.reshape(-1, self.cfg.n_input_dims)) + ).reshape(*points.shape[:-1], 1) + + _, density = self.get_activated_density(points_unscaled, density) + return density + + def forward_field( + self, points: Float[Tensor, "*N Di"] + ) -> Tuple[Float[Tensor, "*N 1"], Optional[Float[Tensor, "*N 3"]]]: + if self.cfg.isosurface_deformable_grid: + threestudio.warn( + f"{self.__class__.__name__} does not support isosurface_deformable_grid. Ignoring." + ) + density = self.forward_density(points) + return density, None + + def forward_level( + self, field: Float[Tensor, "*N 1"], threshold: float + ) -> Float[Tensor, "*N 1"]: + return -(field - threshold) + + def export(self, points: Float[Tensor, "*N Di"], **kwargs) -> Dict[str, Any]: + out: Dict[str, Any] = {} + if self.cfg.n_feature_dims == 0: + return out + points_unscaled = points + points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded) + enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims)) + features = self.feature_network(enc).view( + *points.shape[:-1], self.cfg.n_feature_dims + ) + out.update( + { + "features": features, + } + ) + return out + + @staticmethod + @torch.no_grad() + def create_from( + other: BaseGeometry, + cfg: Optional[Union[dict, DictConfig]] = None, + copy_net: bool = True, + **kwargs, + ) -> "ImplicitVolume": + if isinstance(other, ImplicitVolume): + instance = ImplicitVolume(cfg, **kwargs) + instance.encoding.load_state_dict(other.encoding.state_dict()) + instance.density_network.load_state_dict(other.density_network.state_dict()) + if copy_net: + if ( + instance.cfg.n_feature_dims > 0 + and other.cfg.n_feature_dims == instance.cfg.n_feature_dims + ): + instance.feature_network.load_state_dict( + other.feature_network.state_dict() + ) + if ( + instance.cfg.normal_type == "pred" + and other.cfg.normal_type == "pred" + ): + instance.normal_network.load_state_dict( + other.normal_network.state_dict() + ) + return instance + else: + raise TypeError( + f"Cannot create {ImplicitVolume.__name__} from {other.__class__.__name__}" + ) + + def update_step( + self, epoch: int, global_step: int, on_load_weights: bool = False + ) -> None: + if self.cfg.anneal_density_blob_std_config is not None: + min_step = self.cfg.anneal_density_blob_std_config.min_anneal_step + max_step = self.cfg.anneal_density_blob_std_config.max_anneal_step + if global_step >= min_step and global_step <= max_step: + end_val = self.cfg.anneal_density_blob_std_config.end_val + start_val = self.cfg.anneal_density_blob_std_config.start_val + self.density_blob_std = start_val + (global_step - min_step) * ( + end_val - start_val + ) / (max_step - min_step) diff --git a/threestudio/models/geometry/tetrahedra_sdf_grid.py b/threestudio/models/geometry/tetrahedra_sdf_grid.py new file mode 100644 index 0000000..71f5c07 --- /dev/null +++ b/threestudio/models/geometry/tetrahedra_sdf_grid.py @@ -0,0 +1,369 @@ +import os +from dataclasses import dataclass, field + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.models.geometry.base import ( + BaseExplicitGeometry, + BaseGeometry, + contract_to_unisphere, +) +from threestudio.models.geometry.implicit_sdf import ImplicitSDF +from threestudio.models.geometry.implicit_volume import ImplicitVolume +from threestudio.models.isosurface import MarchingTetrahedraHelper +from threestudio.models.mesh import Mesh +from threestudio.models.networks import get_encoding, get_mlp +from threestudio.utils.misc import broadcast +from threestudio.utils.ops import scale_tensor +from threestudio.utils.typing import * + + +@threestudio.register("tetrahedra-sdf-grid") +class TetrahedraSDFGrid(BaseExplicitGeometry): + @dataclass + class Config(BaseExplicitGeometry.Config): + isosurface_resolution: int = 128 + isosurface_deformable_grid: bool = True + isosurface_remove_outliers: bool = False + isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01 + + n_input_dims: int = 3 + n_feature_dims: int = 3 + pos_encoding_config: dict = field( + default_factory=lambda: { + "otype": "HashGrid", + "n_levels": 16, + "n_features_per_level": 2, + "log2_hashmap_size": 19, + "base_resolution": 16, + "per_level_scale": 1.447269237440378, + } + ) + mlp_network_config: dict = field( + default_factory=lambda: { + "otype": "VanillaMLP", + "activation": "ReLU", + "output_activation": "none", + "n_neurons": 64, + "n_hidden_layers": 1, + } + ) + shape_init: Optional[str] = None + shape_init_params: Optional[Any] = None + shape_init_mesh_up: str = "+z" + shape_init_mesh_front: str = "+x" + force_shape_init: bool = False + geometry_only: bool = False + fix_geometry: bool = False + + cfg: Config + + def configure(self) -> None: + super().configure() + + # this should be saved to state_dict, register as buffer + self.isosurface_bbox: Float[Tensor, "2 3"] + self.register_buffer("isosurface_bbox", self.bbox.clone()) + + self.isosurface_helper = MarchingTetrahedraHelper( + self.cfg.isosurface_resolution, + f"load/tets/{self.cfg.isosurface_resolution}_tets.npz", + ) + + self.sdf: Float[Tensor, "Nv 1"] + self.deformation: Optional[Float[Tensor, "Nv 3"]] + + if not self.cfg.fix_geometry: + self.register_parameter( + "sdf", + nn.Parameter( + torch.zeros( + (self.isosurface_helper.grid_vertices.shape[0], 1), + dtype=torch.float32, + ) + ), + ) + if self.cfg.isosurface_deformable_grid: + self.register_parameter( + "deformation", + nn.Parameter( + torch.zeros_like(self.isosurface_helper.grid_vertices) + ), + ) + else: + self.deformation = None + else: + self.register_buffer( + "sdf", + torch.zeros( + (self.isosurface_helper.grid_vertices.shape[0], 1), + dtype=torch.float32, + ), + ) + if self.cfg.isosurface_deformable_grid: + self.register_buffer( + "deformation", + torch.zeros_like(self.isosurface_helper.grid_vertices), + ) + else: + self.deformation = None + + if not self.cfg.geometry_only: + self.encoding = get_encoding( + self.cfg.n_input_dims, self.cfg.pos_encoding_config + ) + self.feature_network = get_mlp( + self.encoding.n_output_dims, + self.cfg.n_feature_dims, + self.cfg.mlp_network_config, + ) + + self.mesh: Optional[Mesh] = None + + def initialize_shape(self) -> None: + if self.cfg.shape_init is None and not self.cfg.force_shape_init: + return + + # do not initialize shape if weights are provided + if self.cfg.weights is not None and not self.cfg.force_shape_init: + return + + get_gt_sdf: Callable[[Float[Tensor, "N 3"]], Float[Tensor, "N 1"]] + assert isinstance(self.cfg.shape_init, str) + if self.cfg.shape_init == "ellipsoid": + assert ( + isinstance(self.cfg.shape_init_params, Sized) + and len(self.cfg.shape_init_params) == 3 + ) + size = torch.as_tensor(self.cfg.shape_init_params).to(self.device) + + def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]: + return ((points_rand / size) ** 2).sum( + dim=-1, keepdim=True + ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid + + get_gt_sdf = func + elif self.cfg.shape_init == "sphere": + assert isinstance(self.cfg.shape_init_params, float) + radius = self.cfg.shape_init_params + + def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]: + return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius + + get_gt_sdf = func + elif self.cfg.shape_init.startswith("mesh:"): + assert isinstance(self.cfg.shape_init_params, float) + mesh_path = self.cfg.shape_init[5:] + if not os.path.exists(mesh_path): + raise ValueError(f"Mesh file {mesh_path} does not exist.") + + import trimesh + + mesh = trimesh.load(mesh_path) + + # move to center + centroid = mesh.vertices.mean(0) + mesh.vertices = mesh.vertices - centroid + + # align to up-z and front-x + dirs = ["+x", "+y", "+z", "-x", "-y", "-z"] + dir2vec = { + "+x": np.array([1, 0, 0]), + "+y": np.array([0, 1, 0]), + "+z": np.array([0, 0, 1]), + "-x": np.array([-1, 0, 0]), + "-y": np.array([0, -1, 0]), + "-z": np.array([0, 0, -1]), + } + if ( + self.cfg.shape_init_mesh_up not in dirs + or self.cfg.shape_init_mesh_front not in dirs + ): + raise ValueError( + f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}." + ) + if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]: + raise ValueError( + "shape_init_mesh_up and shape_init_mesh_front must be orthogonal." + ) + z_, x_ = ( + dir2vec[self.cfg.shape_init_mesh_up], + dir2vec[self.cfg.shape_init_mesh_front], + ) + y_ = np.cross(z_, x_) + std2mesh = np.stack([x_, y_, z_], axis=0).T + mesh2std = np.linalg.inv(std2mesh) + + # scaling + scale = np.abs(mesh.vertices).max() + mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params + mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T + + from pysdf import SDF + + sdf = SDF(mesh.vertices, mesh.faces) + + def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]: + # add a negative signed here + # as in pysdf the inside of the shape has positive signed distance + return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to( + points_rand + )[..., None] + + get_gt_sdf = func + + else: + raise ValueError( + f"Unknown shape initialization type: {self.cfg.shape_init}" + ) + + sdf_gt = get_gt_sdf( + scale_tensor( + self.isosurface_helper.grid_vertices, + self.isosurface_helper.points_range, + self.isosurface_bbox, + ) + ) + self.sdf.data = sdf_gt + + # explicit broadcast to ensure param consistency across ranks + for param in self.parameters(): + broadcast(param, src=0) + + def isosurface(self) -> Mesh: + # return cached mesh if fix_geometry is True to save computation + if self.cfg.fix_geometry and self.mesh is not None: + return self.mesh + mesh = self.isosurface_helper(self.sdf, self.deformation) + mesh.v_pos = scale_tensor( + mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox + ) + if self.cfg.isosurface_remove_outliers: + mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold) + self.mesh = mesh + return mesh + + def forward( + self, points: Float[Tensor, "*N Di"], output_normal: bool = False + ) -> Dict[str, Float[Tensor, "..."]]: + if self.cfg.geometry_only: + return {} + assert ( + output_normal == False + ), f"Normal output is not supported for {self.__class__.__name__}" + points_unscaled = points # points in the original scale + points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1) + enc = self.encoding(points.view(-1, self.cfg.n_input_dims)) + features = self.feature_network(enc).view( + *points.shape[:-1], self.cfg.n_feature_dims + ) + return {"features": features} + + @staticmethod + @torch.no_grad() + def create_from( + other: BaseGeometry, + cfg: Optional[Union[dict, DictConfig]] = None, + copy_net: bool = True, + **kwargs, + ) -> "TetrahedraSDFGrid": + if isinstance(other, TetrahedraSDFGrid): + instance = TetrahedraSDFGrid(cfg, **kwargs) + assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution + instance.isosurface_bbox = other.isosurface_bbox.clone() + instance.sdf.data = other.sdf.data.clone() + if ( + instance.cfg.isosurface_deformable_grid + and other.cfg.isosurface_deformable_grid + ): + assert ( + instance.deformation is not None and other.deformation is not None + ) + instance.deformation.data = other.deformation.data.clone() + if ( + not instance.cfg.geometry_only + and not other.cfg.geometry_only + and copy_net + ): + instance.encoding.load_state_dict(other.encoding.state_dict()) + instance.feature_network.load_state_dict( + other.feature_network.state_dict() + ) + return instance + elif isinstance(other, ImplicitVolume): + instance = TetrahedraSDFGrid(cfg, **kwargs) + if other.cfg.isosurface_method != "mt": + other.cfg.isosurface_method = "mt" + threestudio.warn( + f"Override isosurface_method of the source geometry to 'mt'" + ) + if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution: + other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution + threestudio.warn( + f"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}" + ) + mesh = other.isosurface() + instance.isosurface_bbox = mesh.extras["bbox"] + instance.sdf.data = ( + mesh.extras["grid_level"].to(instance.sdf.data).clamp(-1, 1) + ) + if not instance.cfg.geometry_only and copy_net: + instance.encoding.load_state_dict(other.encoding.state_dict()) + instance.feature_network.load_state_dict( + other.feature_network.state_dict() + ) + return instance + elif isinstance(other, ImplicitSDF): + instance = TetrahedraSDFGrid(cfg, **kwargs) + if other.cfg.isosurface_method != "mt": + other.cfg.isosurface_method = "mt" + threestudio.warn( + f"Override isosurface_method of the source geometry to 'mt'" + ) + if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution: + other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution + threestudio.warn( + f"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}" + ) + mesh = other.isosurface() + instance.isosurface_bbox = mesh.extras["bbox"] + instance.sdf.data = mesh.extras["grid_level"].to(instance.sdf.data) + if ( + instance.cfg.isosurface_deformable_grid + and other.cfg.isosurface_deformable_grid + ): + assert instance.deformation is not None + instance.deformation.data = mesh.extras["grid_deformation"].to( + instance.deformation.data + ) + if not instance.cfg.geometry_only and copy_net: + instance.encoding.load_state_dict(other.encoding.state_dict()) + instance.feature_network.load_state_dict( + other.feature_network.state_dict() + ) + return instance + else: + raise TypeError( + f"Cannot create {TetrahedraSDFGrid.__name__} from {other.__class__.__name__}" + ) + + def export(self, points: Float[Tensor, "*N Di"], **kwargs) -> Dict[str, Any]: + out: Dict[str, Any] = {} + if self.cfg.geometry_only or self.cfg.n_feature_dims == 0: + return out + points_unscaled = points + points = contract_to_unisphere(points_unscaled, self.bbox) + enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims)) + features = self.feature_network(enc).view( + *points.shape[:-1], self.cfg.n_feature_dims + ) + out.update( + { + "features": features, + } + ) + return out diff --git a/threestudio/models/geometry/volume_grid.py b/threestudio/models/geometry/volume_grid.py new file mode 100644 index 0000000..ae258a1 --- /dev/null +++ b/threestudio/models/geometry/volume_grid.py @@ -0,0 +1,190 @@ +from dataclasses import dataclass, field + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.models.geometry.base import BaseImplicitGeometry, contract_to_unisphere +from threestudio.utils.ops import get_activation +from threestudio.utils.typing import * + + +@threestudio.register("volume-grid") +class VolumeGrid(BaseImplicitGeometry): + @dataclass + class Config(BaseImplicitGeometry.Config): + grid_size: Tuple[int, int, int] = field(default_factory=lambda: (100, 100, 100)) + n_feature_dims: int = 3 + density_activation: Optional[str] = "softplus" + density_bias: Union[float, str] = "blob" + density_blob_scale: float = 5.0 + density_blob_std: float = 0.5 + normal_type: Optional[ + str + ] = "finite_difference" # in ['pred', 'finite_difference', 'finite_difference_laplacian'] + + # automatically determine the threshold + isosurface_threshold: Union[float, str] = "auto" + + cfg: Config + + def configure(self) -> None: + super().configure() + self.grid_size = self.cfg.grid_size + + self.grid = nn.Parameter( + torch.zeros(1, self.cfg.n_feature_dims + 1, *self.grid_size) + ) + if self.cfg.density_bias == "blob": + self.register_buffer("density_scale", torch.tensor(0.0)) + else: + self.density_scale = nn.Parameter(torch.tensor(0.0)) + + if self.cfg.normal_type == "pred": + self.normal_grid = nn.Parameter(torch.zeros(1, 3, *self.grid_size)) + + def get_density_bias(self, points: Float[Tensor, "*N Di"]): + if self.cfg.density_bias == "blob": + # density_bias: Float[Tensor, "*N 1"] = self.cfg.density_blob_scale * torch.exp(-0.5 * (points ** 2).sum(dim=-1) / self.cfg.density_blob_std ** 2)[...,None] + density_bias: Float[Tensor, "*N 1"] = ( + self.cfg.density_blob_scale + * ( + 1 + - torch.sqrt((points.detach() ** 2).sum(dim=-1)) + / self.cfg.density_blob_std + )[..., None] + ) + return density_bias + elif isinstance(self.cfg.density_bias, float): + return self.cfg.density_bias + else: + raise AttributeError(f"Unknown density bias {self.cfg.density_bias}") + + def get_trilinear_feature( + self, points: Float[Tensor, "*N Di"], grid: Float[Tensor, "1 Df G1 G2 G3"] + ) -> Float[Tensor, "*N Df"]: + points_shape = points.shape[:-1] + df = grid.shape[1] + di = points.shape[-1] + out = F.grid_sample( + grid, points.view(1, 1, 1, -1, di), align_corners=False, mode="bilinear" + ) + out = out.reshape(df, -1).T.reshape(*points_shape, df) + return out + + def forward( + self, points: Float[Tensor, "*N Di"], output_normal: bool = False + ) -> Dict[str, Float[Tensor, "..."]]: + points_unscaled = points # points in the original scale + points = contract_to_unisphere( + points, self.bbox, self.unbounded + ) # points normalized to (0, 1) + points = points * 2 - 1 # convert to [-1, 1] for grid sample + + out = self.get_trilinear_feature(points, self.grid) + density, features = out[..., 0:1], out[..., 1:] + density = density * torch.exp(self.density_scale) # exp scaling in DreamFusion + + # breakpoint() + density = get_activation(self.cfg.density_activation)( + density + self.get_density_bias(points_unscaled) + ) + + output = { + "density": density, + "features": features, + } + + if output_normal: + if ( + self.cfg.normal_type == "finite_difference" + or self.cfg.normal_type == "finite_difference_laplacian" + ): + eps = 1.0e-3 + if self.cfg.normal_type == "finite_difference_laplacian": + offsets: Float[Tensor, "6 3"] = torch.as_tensor( + [ + [eps, 0.0, 0.0], + [-eps, 0.0, 0.0], + [0.0, eps, 0.0], + [0.0, -eps, 0.0], + [0.0, 0.0, eps], + [0.0, 0.0, -eps], + ] + ).to(points_unscaled) + points_offset: Float[Tensor, "... 6 3"] = ( + points_unscaled[..., None, :] + offsets + ).clamp(-self.cfg.radius, self.cfg.radius) + density_offset: Float[Tensor, "... 6 1"] = self.forward_density( + points_offset + ) + normal = ( + -0.5 + * (density_offset[..., 0::2, 0] - density_offset[..., 1::2, 0]) + / eps + ) + else: + offsets: Float[Tensor, "3 3"] = torch.as_tensor( + [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]] + ).to(points_unscaled) + points_offset: Float[Tensor, "... 3 3"] = ( + points_unscaled[..., None, :] + offsets + ).clamp(-self.cfg.radius, self.cfg.radius) + density_offset: Float[Tensor, "... 3 1"] = self.forward_density( + points_offset + ) + normal = -(density_offset[..., 0::1, 0] - density) / eps + normal = F.normalize(normal, dim=-1) + elif self.cfg.normal_type == "pred": + normal = self.get_trilinear_feature(points, self.normal_grid) + normal = F.normalize(normal, dim=-1) + else: + raise AttributeError(f"Unknown normal type {self.cfg.normal_type}") + output.update({"normal": normal, "shading_normal": normal}) + return output + + def forward_density(self, points: Float[Tensor, "*N Di"]) -> Float[Tensor, "*N 1"]: + points_unscaled = points + points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded) + points = points * 2 - 1 # convert to [-1, 1] for grid sample + + out = self.get_trilinear_feature(points, self.grid) + density = out[..., 0:1] + density = density * torch.exp(self.density_scale) + + density = get_activation(self.cfg.density_activation)( + density + self.get_density_bias(points_unscaled) + ) + return density + + def forward_field( + self, points: Float[Tensor, "*N Di"] + ) -> Tuple[Float[Tensor, "*N 1"], Optional[Float[Tensor, "*N 3"]]]: + if self.cfg.isosurface_deformable_grid: + threestudio.warn( + f"{self.__class__.__name__} does not support isosurface_deformable_grid. Ignoring." + ) + density = self.forward_density(points) + return density, None + + def forward_level( + self, field: Float[Tensor, "*N 1"], threshold: float + ) -> Float[Tensor, "*N 1"]: + return -(field - threshold) + + def export(self, points: Float[Tensor, "*N Di"], **kwargs) -> Dict[str, Any]: + out: Dict[str, Any] = {} + if self.cfg.n_feature_dims == 0: + return out + points_unscaled = points + points = contract_to_unisphere(points, self.bbox, self.unbounded) + points = points * 2 - 1 # convert to [-1, 1] for grid sample + features = self.get_trilinear_feature(points, self.grid)[..., 1:] + out.update( + { + "features": features, + } + ) + return out diff --git a/threestudio/models/guidance/__init__.py b/threestudio/models/guidance/__init__.py new file mode 100644 index 0000000..b25a8d7 --- /dev/null +++ b/threestudio/models/guidance/__init__.py @@ -0,0 +1,11 @@ +from . import ( + controlnet_guidance, + deep_floyd_guidance, + instructpix2pix_guidance, + stable_diffusion_guidance, + stable_diffusion_unified_guidance, + stable_diffusion_vsd_guidance, + stable_zero123_guidance, + zero123_guidance, + zero123_unified_guidance, +) diff --git a/threestudio/models/guidance/controlnet_guidance.py b/threestudio/models/guidance/controlnet_guidance.py new file mode 100644 index 0000000..ac014c7 --- /dev/null +++ b/threestudio/models/guidance/controlnet_guidance.py @@ -0,0 +1,427 @@ +import os +from dataclasses import dataclass + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +from controlnet_aux import CannyDetector, NormalBaeDetector +from diffusers import ControlNetModel, DDIMScheduler, StableDiffusionControlNetPipeline +from diffusers.utils.import_utils import is_xformers_available +from tqdm import tqdm + +import threestudio +from threestudio.models.prompt_processors.base import PromptProcessorOutput +from threestudio.utils.base import BaseObject +from threestudio.utils.misc import C, parse_version +from threestudio.utils.typing import * + + +@threestudio.register("stable-diffusion-controlnet-guidance") +class ControlNetGuidance(BaseObject): + @dataclass + class Config(BaseObject.Config): + cache_dir: Optional[str] = None + pretrained_model_name_or_path: str = "SG161222/Realistic_Vision_V2.0" + ddim_scheduler_name_or_path: str = "runwayml/stable-diffusion-v1-5" + control_type: str = "normal" # normal/canny + + enable_memory_efficient_attention: bool = False + enable_sequential_cpu_offload: bool = False + enable_attention_slicing: bool = False + enable_channels_last_format: bool = False + guidance_scale: float = 7.5 + condition_scale: float = 1.5 + grad_clip: Optional[ + Any + ] = None # field(default_factory=lambda: [0, 2.0, 8.0, 1000]) + half_precision_weights: bool = True + + fixed_size: int = -1 + + min_step_percent: float = 0.02 + max_step_percent: float = 0.98 + + diffusion_steps: int = 20 + + use_sds: bool = False + + # Canny threshold + canny_lower_bound: int = 50 + canny_upper_bound: int = 100 + + cfg: Config + + def configure(self) -> None: + threestudio.info(f"Loading ControlNet ...") + + controlnet_name_or_path: str + if self.cfg.control_type == "normal": + controlnet_name_or_path = "lllyasviel/control_v11p_sd15_normalbae" + elif self.cfg.control_type == "canny": + controlnet_name_or_path = "lllyasviel/control_v11p_sd15_canny" + + self.weights_dtype = ( + torch.float16 if self.cfg.half_precision_weights else torch.float32 + ) + + pipe_kwargs = { + "safety_checker": None, + "feature_extractor": None, + "requires_safety_checker": False, + "torch_dtype": self.weights_dtype, + "cache_dir": self.cfg.cache_dir, + } + + controlnet = ControlNetModel.from_pretrained( + controlnet_name_or_path, + torch_dtype=self.weights_dtype, + cache_dir=self.cfg.cache_dir, + ) + self.pipe = StableDiffusionControlNetPipeline.from_pretrained( + self.cfg.pretrained_model_name_or_path, controlnet=controlnet, **pipe_kwargs + ).to(self.device) + self.scheduler = DDIMScheduler.from_pretrained( + self.cfg.ddim_scheduler_name_or_path, + subfolder="scheduler", + torch_dtype=self.weights_dtype, + cache_dir=self.cfg.cache_dir, + ) + self.scheduler.set_timesteps(self.cfg.diffusion_steps) + + if self.cfg.enable_memory_efficient_attention: + if parse_version(torch.__version__) >= parse_version("2"): + threestudio.info( + "PyTorch2.0 uses memory efficient attention by default." + ) + elif not is_xformers_available(): + threestudio.warn( + "xformers is not available, memory efficient attention is not enabled." + ) + else: + self.pipe.enable_xformers_memory_efficient_attention() + + if self.cfg.enable_sequential_cpu_offload: + self.pipe.enable_sequential_cpu_offload() + + if self.cfg.enable_attention_slicing: + self.pipe.enable_attention_slicing(1) + + if self.cfg.enable_channels_last_format: + self.pipe.unet.to(memory_format=torch.channels_last) + + # Create model + self.vae = self.pipe.vae.eval() + self.unet = self.pipe.unet.eval() + self.controlnet = self.pipe.controlnet.eval() + + if self.cfg.control_type == "normal": + self.preprocessor = NormalBaeDetector.from_pretrained( + "lllyasviel/Annotators" + ) + self.preprocessor.model.to(self.device) + elif self.cfg.control_type == "canny": + self.preprocessor = CannyDetector() + + for p in self.vae.parameters(): + p.requires_grad_(False) + for p in self.unet.parameters(): + p.requires_grad_(False) + + self.num_train_timesteps = self.scheduler.config.num_train_timesteps + self.set_min_max_steps() # set to default value + + self.alphas: Float[Tensor, "..."] = self.scheduler.alphas_cumprod.to( + self.device + ) + + self.grad_clip_val: Optional[float] = None + + threestudio.info(f"Loaded ControlNet!") + + @torch.cuda.amp.autocast(enabled=False) + def set_min_max_steps(self, min_step_percent=0.02, max_step_percent=0.98): + self.min_step = int(self.num_train_timesteps * min_step_percent) + self.max_step = int(self.num_train_timesteps * max_step_percent) + + @torch.cuda.amp.autocast(enabled=False) + def forward_controlnet( + self, + latents: Float[Tensor, "..."], + t: Float[Tensor, "..."], + image_cond: Float[Tensor, "..."], + condition_scale: float, + encoder_hidden_states: Float[Tensor, "..."], + ) -> Float[Tensor, "..."]: + return self.controlnet( + latents.to(self.weights_dtype), + t.to(self.weights_dtype), + encoder_hidden_states=encoder_hidden_states.to(self.weights_dtype), + controlnet_cond=image_cond.to(self.weights_dtype), + conditioning_scale=condition_scale, + return_dict=False, + ) + + @torch.cuda.amp.autocast(enabled=False) + def forward_control_unet( + self, + latents: Float[Tensor, "..."], + t: Float[Tensor, "..."], + encoder_hidden_states: Float[Tensor, "..."], + cross_attention_kwargs, + down_block_additional_residuals, + mid_block_additional_residual, + ) -> Float[Tensor, "..."]: + input_dtype = latents.dtype + return self.unet( + latents.to(self.weights_dtype), + t.to(self.weights_dtype), + encoder_hidden_states=encoder_hidden_states.to(self.weights_dtype), + cross_attention_kwargs=cross_attention_kwargs, + down_block_additional_residuals=down_block_additional_residuals, + mid_block_additional_residual=mid_block_additional_residual, + ).sample.to(input_dtype) + + @torch.cuda.amp.autocast(enabled=False) + def encode_images( + self, imgs: Float[Tensor, "B 3 H W"] + ) -> Float[Tensor, "B 4 DH DW"]: + input_dtype = imgs.dtype + imgs = imgs * 2.0 - 1.0 + posterior = self.vae.encode(imgs.to(self.weights_dtype)).latent_dist + latents = posterior.sample() * self.vae.config.scaling_factor + return latents.to(input_dtype) + + @torch.cuda.amp.autocast(enabled=False) + def encode_cond_images( + self, imgs: Float[Tensor, "B 3 H W"] + ) -> Float[Tensor, "B 4 DH DW"]: + input_dtype = imgs.dtype + imgs = imgs * 2.0 - 1.0 + posterior = self.vae.encode(imgs.to(self.weights_dtype)).latent_dist + latents = posterior.mode() + uncond_image_latents = torch.zeros_like(latents) + latents = torch.cat([latents, latents, uncond_image_latents], dim=0) + return latents.to(input_dtype) + + @torch.cuda.amp.autocast(enabled=False) + def decode_latents( + self, latents: Float[Tensor, "B 4 DH DW"] + ) -> Float[Tensor, "B 3 H W"]: + input_dtype = latents.dtype + latents = 1 / self.vae.config.scaling_factor * latents + image = self.vae.decode(latents.to(self.weights_dtype)).sample + image = (image * 0.5 + 0.5).clamp(0, 1) + return image.to(input_dtype) + + def edit_latents( + self, + text_embeddings: Float[Tensor, "BB 77 768"], + latents: Float[Tensor, "B 4 DH DW"], + image_cond: Float[Tensor, "B 3 H W"], + t: Int[Tensor, "B"], + ) -> Float[Tensor, "B 4 DH DW"]: + self.scheduler.config.num_train_timesteps = t.item() + self.scheduler.set_timesteps(self.cfg.diffusion_steps) + with torch.no_grad(): + # add noise + noise = torch.randn_like(latents) + latents = self.scheduler.add_noise(latents, noise, t) # type: ignore + + # sections of code used from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py + threestudio.debug("Start editing...") + for i, t in enumerate(self.scheduler.timesteps): + # predict the noise residual with unet, NO grad! + with torch.no_grad(): + # pred noise + latent_model_input = torch.cat([latents] * 2) + ( + down_block_res_samples, + mid_block_res_sample, + ) = self.forward_controlnet( + latent_model_input, + t, + encoder_hidden_states=text_embeddings, + image_cond=image_cond, + condition_scale=self.cfg.condition_scale, + ) + + noise_pred = self.forward_control_unet( + latent_model_input, + t, + encoder_hidden_states=text_embeddings, + cross_attention_kwargs=None, + down_block_additional_residuals=down_block_res_samples, + mid_block_additional_residual=mid_block_res_sample, + ) + # perform classifier-free guidance + noise_pred_text, noise_pred_uncond = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + self.cfg.guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + # get previous sample, continue loop + latents = self.scheduler.step(noise_pred, t, latents).prev_sample + threestudio.debug("Editing finished.") + return latents + + def prepare_image_cond(self, cond_rgb: Float[Tensor, "B H W C"]): + if self.cfg.control_type == "normal": + cond_rgb = ( + (cond_rgb[0].detach().cpu().numpy() * 255).astype(np.uint8).copy() + ) + detected_map = self.preprocessor(cond_rgb) + control = ( + torch.from_numpy(np.array(detected_map)).float().to(self.device) / 255.0 + ) + control = control.unsqueeze(0) + control = control.permute(0, 3, 1, 2) + elif self.cfg.control_type == "canny": + cond_rgb = ( + (cond_rgb[0].detach().cpu().numpy() * 255).astype(np.uint8).copy() + ) + blurred_img = cv2.blur(cond_rgb, ksize=(5, 5)) + detected_map = self.preprocessor( + blurred_img, self.cfg.canny_lower_bound, self.cfg.canny_upper_bound + ) + control = ( + torch.from_numpy(np.array(detected_map)).float().to(self.device) / 255.0 + ) + control = control.unsqueeze(-1).repeat(1, 1, 3) + control = control.unsqueeze(0) + control = control.permute(0, 3, 1, 2) + + return control + + def compute_grad_sds( + self, + text_embeddings: Float[Tensor, "BB 77 768"], + latents: Float[Tensor, "B 4 DH DW"], + image_cond: Float[Tensor, "B 3 H W"], + t: Int[Tensor, "B"], + ): + with torch.no_grad(): + # add noise + noise = torch.randn_like(latents) # TODO: use torch generator + latents_noisy = self.scheduler.add_noise(latents, noise, t) + # pred noise + latent_model_input = torch.cat([latents_noisy] * 2) + down_block_res_samples, mid_block_res_sample = self.forward_controlnet( + latent_model_input, + t, + encoder_hidden_states=text_embeddings, + image_cond=image_cond, + condition_scale=self.cfg.condition_scale, + ) + + noise_pred = self.forward_control_unet( + latent_model_input, + t, + encoder_hidden_states=text_embeddings, + cross_attention_kwargs=None, + down_block_additional_residuals=down_block_res_samples, + mid_block_additional_residual=mid_block_res_sample, + ) + + # perform classifier-free guidance + noise_pred_text, noise_pred_uncond = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + self.cfg.guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + + w = (1 - self.alphas[t]).view(-1, 1, 1, 1) + grad = w * (noise_pred - noise) + return grad + + def __call__( + self, + rgb: Float[Tensor, "B H W C"], + cond_rgb: Float[Tensor, "B H W C"], + prompt_utils: PromptProcessorOutput, + **kwargs, + ): + batch_size, H, W, _ = rgb.shape + assert batch_size == 1 + assert rgb.shape[:-1] == cond_rgb.shape[:-1] + + rgb_BCHW = rgb.permute(0, 3, 1, 2) + latents: Float[Tensor, "B 4 DH DW"] + if self.cfg.fixed_size > 0: + RH, RW = self.cfg.fixed_size, self.cfg.fixed_size + else: + RH, RW = H // 8 * 8, W // 8 * 8 + rgb_BCHW_HW8 = F.interpolate( + rgb_BCHW, (RH, RW), mode="bilinear", align_corners=False + ) + latents = self.encode_images(rgb_BCHW_HW8) + + image_cond = self.prepare_image_cond(cond_rgb) + image_cond = F.interpolate( + image_cond, (RH, RW), mode="bilinear", align_corners=False + ) + + temp = torch.zeros(1).to(rgb.device) + text_embeddings = prompt_utils.get_text_embeddings(temp, temp, temp, False) + + # timestep ~ U(0.02, 0.98) to avoid very high/low noise level + t = torch.randint( + self.min_step, + self.max_step + 1, + [batch_size], + dtype=torch.long, + device=self.device, + ) + + if self.cfg.use_sds: + grad = self.compute_grad_sds(text_embeddings, latents, image_cond, t) + grad = torch.nan_to_num(grad) + if self.grad_clip_val is not None: + grad = grad.clamp(-self.grad_clip_val, self.grad_clip_val) + target = (latents - grad).detach() + loss_sds = 0.5 * F.mse_loss(latents, target, reduction="sum") / batch_size + return { + "loss_sds": loss_sds, + "grad_norm": grad.norm(), + "min_step": self.min_step, + "max_step": self.max_step, + } + else: + edit_latents = self.edit_latents(text_embeddings, latents, image_cond, t) + edit_images = self.decode_latents(edit_latents) + edit_images = F.interpolate(edit_images, (H, W), mode="bilinear") + + return {"edit_images": edit_images.permute(0, 2, 3, 1)} + + def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False): + # clip grad for stable training as demonstrated in + # Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation + # http://arxiv.org/abs/2303.15413 + if self.cfg.grad_clip is not None: + self.grad_clip_val = C(self.cfg.grad_clip, epoch, global_step) + + self.set_min_max_steps( + min_step_percent=C(self.cfg.min_step_percent, epoch, global_step), + max_step_percent=C(self.cfg.max_step_percent, epoch, global_step), + ) + + +if __name__ == "__main__": + from threestudio.utils.config import ExperimentConfig, load_config + from threestudio.utils.typing import Optional + + cfg = load_config("configs/debugging/controlnet-normal.yaml") + guidance = threestudio.find(cfg.system.guidance_type)(cfg.system.guidance) + prompt_processor = threestudio.find(cfg.system.prompt_processor_type)( + cfg.system.prompt_processor + ) + + rgb_image = cv2.imread("assets/face.jpg")[:, :, ::-1].copy() / 255 + rgb_image = torch.FloatTensor(rgb_image).unsqueeze(0).to(guidance.device) + prompt_utils = prompt_processor() + guidance_out = guidance(rgb_image, rgb_image, prompt_utils) + edit_image = ( + (guidance_out["edit_images"][0].detach().cpu().clip(0, 1).numpy() * 255) + .astype(np.uint8)[:, :, ::-1] + .copy() + ) + os.makedirs(".threestudio_cache", exist_ok=True) + cv2.imwrite(".threestudio_cache/edit_image.jpg", edit_image) diff --git a/threestudio/models/guidance/deep_floyd_guidance.py b/threestudio/models/guidance/deep_floyd_guidance.py new file mode 100644 index 0000000..fa6ecea --- /dev/null +++ b/threestudio/models/guidance/deep_floyd_guidance.py @@ -0,0 +1,469 @@ +from dataclasses import dataclass, field + +import torch +import torch.nn as nn +import torch.nn.functional as F +from diffusers import IFPipeline +from diffusers.utils.import_utils import is_xformers_available +from tqdm import tqdm + +import threestudio +from threestudio.models.prompt_processors.base import PromptProcessorOutput +from threestudio.utils.base import BaseObject +from threestudio.utils.misc import C, parse_version +from threestudio.utils.ops import perpendicular_component +from threestudio.utils.typing import * + + +@threestudio.register("deep-floyd-guidance") +class DeepFloydGuidance(BaseObject): + @dataclass + class Config(BaseObject.Config): + pretrained_model_name_or_path: str = "DeepFloyd/IF-I-XL-v1.0" + # FIXME: xformers error + enable_memory_efficient_attention: bool = False + enable_sequential_cpu_offload: bool = False + enable_attention_slicing: bool = False + enable_channels_last_format: bool = True + guidance_scale: float = 20.0 + grad_clip: Optional[ + Any + ] = None # field(default_factory=lambda: [0, 2.0, 8.0, 1000]) + half_precision_weights: bool = True + + min_step_percent: float = 0.02 + max_step_percent: float = 0.98 + + weighting_strategy: str = "sds" + + view_dependent_prompting: bool = True + + """Maximum number of batch items to evaluate guidance for (for debugging) and to save on disk. -1 means save all items.""" + max_items_eval: int = 4 + + cfg: Config + + def configure(self) -> None: + threestudio.info(f"Loading Deep Floyd ...") + + self.weights_dtype = ( + torch.float16 if self.cfg.half_precision_weights else torch.float32 + ) + + # Create model + self.pipe = IFPipeline.from_pretrained( + self.cfg.pretrained_model_name_or_path, + text_encoder=None, + safety_checker=None, + watermarker=None, + feature_extractor=None, + requires_safety_checker=False, + variant="fp16" if self.cfg.half_precision_weights else None, + torch_dtype=self.weights_dtype, + ).to(self.device) + + if self.cfg.enable_memory_efficient_attention: + if parse_version(torch.__version__) >= parse_version("2"): + threestudio.info( + "PyTorch2.0 uses memory efficient attention by default." + ) + elif not is_xformers_available(): + threestudio.warn( + "xformers is not available, memory efficient attention is not enabled." + ) + else: + threestudio.warn( + f"Use DeepFloyd with xformers may raise error, see https://github.com/deep-floyd/IF/issues/52 to track this problem." + ) + self.pipe.enable_xformers_memory_efficient_attention() + + if self.cfg.enable_sequential_cpu_offload: + self.pipe.enable_sequential_cpu_offload() + + if self.cfg.enable_attention_slicing: + self.pipe.enable_attention_slicing(1) + + if self.cfg.enable_channels_last_format: + self.pipe.unet.to(memory_format=torch.channels_last) + + self.unet = self.pipe.unet.eval() + + for p in self.unet.parameters(): + p.requires_grad_(False) + + self.scheduler = self.pipe.scheduler + + self.num_train_timesteps = self.scheduler.config.num_train_timesteps + self.set_min_max_steps() # set to default value + + self.alphas: Float[Tensor, "..."] = self.scheduler.alphas_cumprod.to( + self.device + ) + + self.grad_clip_val: Optional[float] = None + + threestudio.info(f"Loaded Deep Floyd!") + + @torch.cuda.amp.autocast(enabled=False) + def set_min_max_steps(self, min_step_percent=0.02, max_step_percent=0.98): + self.min_step = int(self.num_train_timesteps * min_step_percent) + self.max_step = int(self.num_train_timesteps * max_step_percent) + + @torch.cuda.amp.autocast(enabled=False) + def forward_unet( + self, + latents: Float[Tensor, "..."], + t: Float[Tensor, "..."], + encoder_hidden_states: Float[Tensor, "..."], + ) -> Float[Tensor, "..."]: + input_dtype = latents.dtype + return self.unet( + latents.to(self.weights_dtype), + t.to(self.weights_dtype), + encoder_hidden_states=encoder_hidden_states.to(self.weights_dtype), + ).sample.to(input_dtype) + + def __call__( + self, + rgb: Float[Tensor, "B H W C"], + prompt_utils: PromptProcessorOutput, + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + rgb_as_latents=False, + guidance_eval=False, + **kwargs, + ): + batch_size = rgb.shape[0] + + rgb_BCHW = rgb.permute(0, 3, 1, 2) + + assert rgb_as_latents == False, f"No latent space in {self.__class__.__name__}" + rgb_BCHW = rgb_BCHW * 2.0 - 1.0 # scale to [-1, 1] to match the diffusion range + latents = F.interpolate( + rgb_BCHW, (64, 64), mode="bilinear", align_corners=False + ) + + # timestep ~ U(0.02, 0.98) to avoid very high/low noise level + t = torch.randint( + self.min_step, + self.max_step + 1, + [batch_size], + dtype=torch.long, + device=self.device, + ) + + if prompt_utils.use_perp_neg: + ( + text_embeddings, + neg_guidance_weights, + ) = prompt_utils.get_text_embeddings_perp_neg( + elevation, azimuth, camera_distances, self.cfg.view_dependent_prompting + ) + with torch.no_grad(): + noise = torch.randn_like(latents) + latents_noisy = self.scheduler.add_noise(latents, noise, t) + latent_model_input = torch.cat([latents_noisy] * 4, dim=0) + noise_pred = self.forward_unet( + latent_model_input, + torch.cat([t] * 4), + encoder_hidden_states=text_embeddings, + ) # (4B, 6, 64, 64) + + noise_pred_text, _ = noise_pred[:batch_size].split(3, dim=1) + noise_pred_uncond, _ = noise_pred[batch_size : batch_size * 2].split( + 3, dim=1 + ) + noise_pred_neg, _ = noise_pred[batch_size * 2 :].split(3, dim=1) + + e_pos = noise_pred_text - noise_pred_uncond + accum_grad = 0 + n_negative_prompts = neg_guidance_weights.shape[-1] + for i in range(n_negative_prompts): + e_i_neg = noise_pred_neg[i::n_negative_prompts] - noise_pred_uncond + accum_grad += neg_guidance_weights[:, i].view( + -1, 1, 1, 1 + ) * perpendicular_component(e_i_neg, e_pos) + + noise_pred = noise_pred_uncond + self.cfg.guidance_scale * ( + e_pos + accum_grad + ) + else: + neg_guidance_weights = None + text_embeddings = prompt_utils.get_text_embeddings( + elevation, azimuth, camera_distances, self.cfg.view_dependent_prompting + ) + # predict the noise residual with unet, NO grad! + with torch.no_grad(): + # add noise + noise = torch.randn_like(latents) # TODO: use torch generator + latents_noisy = self.scheduler.add_noise(latents, noise, t) + # pred noise + latent_model_input = torch.cat([latents_noisy] * 2, dim=0) + noise_pred = self.forward_unet( + latent_model_input, + torch.cat([t] * 2), + encoder_hidden_states=text_embeddings, + ) # (2B, 6, 64, 64) + + # perform guidance (high scale from paper!) + noise_pred_text, noise_pred_uncond = noise_pred.chunk(2) + noise_pred_text, predicted_variance = noise_pred_text.split(3, dim=1) + noise_pred_uncond, _ = noise_pred_uncond.split(3, dim=1) + noise_pred = noise_pred_text + self.cfg.guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + + """ + # thresholding, experimental + if self.cfg.thresholding: + assert batch_size == 1 + noise_pred = torch.cat([noise_pred, predicted_variance], dim=1) + noise_pred = custom_ddpm_step(self.scheduler, + noise_pred, int(t.item()), latents_noisy, **self.pipe.prepare_extra_step_kwargs(None, 0.0) + ) + """ + + if self.cfg.weighting_strategy == "sds": + # w(t), sigma_t^2 + w = (1 - self.alphas[t]).view(-1, 1, 1, 1) + elif self.cfg.weighting_strategy == "uniform": + w = 1 + elif self.cfg.weighting_strategy == "fantasia3d": + w = (self.alphas[t] ** 0.5 * (1 - self.alphas[t])).view(-1, 1, 1, 1) + else: + raise ValueError( + f"Unknown weighting strategy: {self.cfg.weighting_strategy}" + ) + + grad = w * (noise_pred - noise) + grad = torch.nan_to_num(grad) + # clip grad for stable training? + if self.grad_clip_val is not None: + grad = grad.clamp(-self.grad_clip_val, self.grad_clip_val) + + # loss = SpecifyGradient.apply(latents, grad) + # SpecifyGradient is not straghtforward, use a reparameterization trick instead + target = (latents - grad).detach() + # d(loss)/d(latents) = latents - target = latents - (latents - grad) = grad + loss_sds = 0.5 * F.mse_loss(latents, target, reduction="sum") / batch_size + + guidance_out = { + "loss_sds": loss_sds, + "grad_norm": grad.norm(), + "min_step": self.min_step, + "max_step": self.max_step, + } + + if guidance_eval: + guidance_eval_utils = { + "use_perp_neg": prompt_utils.use_perp_neg, + "neg_guidance_weights": neg_guidance_weights, + "text_embeddings": text_embeddings, + "t_orig": t, + "latents_noisy": latents_noisy, + "noise_pred": torch.cat([noise_pred, predicted_variance], dim=1), + } + guidance_eval_out = self.guidance_eval(**guidance_eval_utils) + texts = [] + for n, e, a, c in zip( + guidance_eval_out["noise_levels"], elevation, azimuth, camera_distances + ): + texts.append( + f"n{n:.02f}\ne{e.item():.01f}\na{a.item():.01f}\nc{c.item():.02f}" + ) + guidance_eval_out.update({"texts": texts}) + guidance_out.update({"eval": guidance_eval_out}) + + return guidance_out + + @torch.cuda.amp.autocast(enabled=False) + @torch.no_grad() + def get_noise_pred( + self, + latents_noisy, + t, + text_embeddings, + use_perp_neg=False, + neg_guidance_weights=None, + ): + batch_size = latents_noisy.shape[0] + if use_perp_neg: + latent_model_input = torch.cat([latents_noisy] * 4, dim=0) + noise_pred = self.forward_unet( + latent_model_input, + torch.cat([t.reshape(1)] * 4).to(self.device), + encoder_hidden_states=text_embeddings, + ) # (4B, 6, 64, 64) + + noise_pred_text, _ = noise_pred[:batch_size].split(3, dim=1) + noise_pred_uncond, _ = noise_pred[batch_size : batch_size * 2].split( + 3, dim=1 + ) + noise_pred_neg, _ = noise_pred[batch_size * 2 :].split(3, dim=1) + + e_pos = noise_pred_text - noise_pred_uncond + accum_grad = 0 + n_negative_prompts = neg_guidance_weights.shape[-1] + for i in range(n_negative_prompts): + e_i_neg = noise_pred_neg[i::n_negative_prompts] - noise_pred_uncond + accum_grad += neg_guidance_weights[:, i].view( + -1, 1, 1, 1 + ) * perpendicular_component(e_i_neg, e_pos) + + noise_pred = noise_pred_uncond + self.cfg.guidance_scale * ( + e_pos + accum_grad + ) + else: + latent_model_input = torch.cat([latents_noisy] * 2, dim=0) + noise_pred = self.forward_unet( + latent_model_input, + torch.cat([t.reshape(1)] * 2).to(self.device), + encoder_hidden_states=text_embeddings, + ) # (2B, 6, 64, 64) + + # perform guidance (high scale from paper!) + noise_pred_text, noise_pred_uncond = noise_pred.chunk(2) + noise_pred_text, predicted_variance = noise_pred_text.split(3, dim=1) + noise_pred_uncond, _ = noise_pred_uncond.split(3, dim=1) + noise_pred = noise_pred_text + self.cfg.guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + + return torch.cat([noise_pred, predicted_variance], dim=1) + + @torch.cuda.amp.autocast(enabled=False) + @torch.no_grad() + def guidance_eval( + self, + t_orig, + text_embeddings, + latents_noisy, + noise_pred, + use_perp_neg=False, + neg_guidance_weights=None, + ): + # use only 50 timesteps, and find nearest of those to t + self.scheduler.set_timesteps(50) + self.scheduler.timesteps_gpu = self.scheduler.timesteps.to(self.device) + bs = ( + min(self.cfg.max_items_eval, latents_noisy.shape[0]) + if self.cfg.max_items_eval > 0 + else latents_noisy.shape[0] + ) # batch size + large_enough_idxs = self.scheduler.timesteps_gpu.expand([bs, -1]) > t_orig[ + :bs + ].unsqueeze( + -1 + ) # sized [bs,50] > [bs,1] + idxs = torch.min(large_enough_idxs, dim=1)[1] + t = self.scheduler.timesteps_gpu[idxs] + + fracs = list((t / self.scheduler.config.num_train_timesteps).cpu().numpy()) + imgs_noisy = (latents_noisy[:bs] / 2 + 0.5).permute(0, 2, 3, 1) + + # get prev latent + latents_1step = [] + pred_1orig = [] + for b in range(bs): + step_output = self.scheduler.step( + noise_pred[b : b + 1], t[b], latents_noisy[b : b + 1] + ) + latents_1step.append(step_output["prev_sample"]) + pred_1orig.append(step_output["pred_original_sample"]) + latents_1step = torch.cat(latents_1step) + pred_1orig = torch.cat(pred_1orig) + imgs_1step = (latents_1step / 2 + 0.5).permute(0, 2, 3, 1) + imgs_1orig = (pred_1orig / 2 + 0.5).permute(0, 2, 3, 1) + + latents_final = [] + for b, i in enumerate(idxs): + latents = latents_1step[b : b + 1] + text_emb = ( + text_embeddings[ + [b, b + len(idxs), b + 2 * len(idxs), b + 3 * len(idxs)], ... + ] + if use_perp_neg + else text_embeddings[[b, b + len(idxs)], ...] + ) + neg_guid = neg_guidance_weights[b : b + 1] if use_perp_neg else None + for t in tqdm(self.scheduler.timesteps[i + 1 :], leave=False): + # pred noise + noise_pred = self.get_noise_pred( + latents, t, text_emb, use_perp_neg, neg_guid + ) + # get prev latent + latents = self.scheduler.step(noise_pred, t, latents)["prev_sample"] + latents_final.append(latents) + + latents_final = torch.cat(latents_final) + imgs_final = (latents_final / 2 + 0.5).permute(0, 2, 3, 1) + + return { + "bs": bs, + "noise_levels": fracs, + "imgs_noisy": imgs_noisy, + "imgs_1step": imgs_1step, + "imgs_1orig": imgs_1orig, + "imgs_final": imgs_final, + } + + def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False): + # clip grad for stable training as demonstrated in + # Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation + # http://arxiv.org/abs/2303.15413 + if self.cfg.grad_clip is not None: + self.grad_clip_val = C(self.cfg.grad_clip, epoch, global_step) + + self.set_min_max_steps( + min_step_percent=C(self.cfg.min_step_percent, epoch, global_step), + max_step_percent=C(self.cfg.max_step_percent, epoch, global_step), + ) + + +""" +# used by thresholding, experimental +def custom_ddpm_step(ddpm, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor, generator=None, return_dict: bool = True): + self = ddpm + t = timestep + + prev_t = self.previous_timestep(t) + + if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]: + model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1) + else: + predicted_variance = None + + # 1. compute alphas, betas + alpha_prod_t = self.alphas_cumprod[t].item() + alpha_prod_t_prev = self.alphas_cumprod[prev_t].item() if prev_t >= 0 else 1.0 + beta_prod_t = 1 - alpha_prod_t + beta_prod_t_prev = 1 - alpha_prod_t_prev + current_alpha_t = alpha_prod_t / alpha_prod_t_prev + current_beta_t = 1 - current_alpha_t + + # 2. compute predicted original sample from predicted noise also called + # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf + if self.config.prediction_type == "epsilon": + pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5) + elif self.config.prediction_type == "sample": + pred_original_sample = model_output + elif self.config.prediction_type == "v_prediction": + pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output + else: + raise ValueError( + f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or" + " `v_prediction` for the DDPMScheduler." + ) + + # 3. Clip or threshold "predicted x_0" + if self.config.thresholding: + pred_original_sample = self._threshold_sample(pred_original_sample) + elif self.config.clip_sample: + pred_original_sample = pred_original_sample.clamp( + -self.config.clip_sample_range, self.config.clip_sample_range + ) + + noise_thresholded = (sample - (alpha_prod_t ** 0.5) * pred_original_sample) / (beta_prod_t ** 0.5) + return noise_thresholded +""" diff --git a/threestudio/models/guidance/instructpix2pix_guidance.py b/threestudio/models/guidance/instructpix2pix_guidance.py new file mode 100644 index 0000000..8fa19fa --- /dev/null +++ b/threestudio/models/guidance/instructpix2pix_guidance.py @@ -0,0 +1,347 @@ +from dataclasses import dataclass + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +from diffusers import DDIMScheduler, StableDiffusionInstructPix2PixPipeline +from diffusers.utils.import_utils import is_xformers_available +from tqdm import tqdm + +import threestudio +from threestudio.models.prompt_processors.base import PromptProcessorOutput +from threestudio.utils.base import BaseObject +from threestudio.utils.misc import C, parse_version +from threestudio.utils.typing import * + + +@threestudio.register("stable-diffusion-instructpix2pix-guidance") +class InstructPix2PixGuidance(BaseObject): + @dataclass + class Config(BaseObject.Config): + cache_dir: Optional[str] = None + ddim_scheduler_name_or_path: str = "CompVis/stable-diffusion-v1-4" + ip2p_name_or_path: str = "timbrooks/instruct-pix2pix" + + enable_memory_efficient_attention: bool = False + enable_sequential_cpu_offload: bool = False + enable_attention_slicing: bool = False + enable_channels_last_format: bool = False + guidance_scale: float = 7.5 + condition_scale: float = 1.5 + grad_clip: Optional[ + Any + ] = None # field(default_factory=lambda: [0, 2.0, 8.0, 1000]) + half_precision_weights: bool = True + + fixed_size: int = -1 + + min_step_percent: float = 0.02 + max_step_percent: float = 0.98 + + diffusion_steps: int = 20 + + use_sds: bool = False + + cfg: Config + + def configure(self) -> None: + threestudio.info(f"Loading InstructPix2Pix ...") + + self.weights_dtype = ( + torch.float16 if self.cfg.half_precision_weights else torch.float32 + ) + + pipe_kwargs = { + "safety_checker": None, + "feature_extractor": None, + "requires_safety_checker": False, + "torch_dtype": self.weights_dtype, + "cache_dir": self.cfg.cache_dir, + } + + self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( + self.cfg.ip2p_name_or_path, **pipe_kwargs + ).to(self.device) + self.scheduler = DDIMScheduler.from_pretrained( + self.cfg.ddim_scheduler_name_or_path, + subfolder="scheduler", + torch_dtype=self.weights_dtype, + cache_dir=self.cfg.cache_dir, + ) + self.scheduler.set_timesteps(self.cfg.diffusion_steps) + + if self.cfg.enable_memory_efficient_attention: + if parse_version(torch.__version__) >= parse_version("2"): + threestudio.info( + "PyTorch2.0 uses memory efficient attention by default." + ) + elif not is_xformers_available(): + threestudio.warn( + "xformers is not available, memory efficient attention is not enabled." + ) + else: + self.pipe.enable_xformers_memory_efficient_attention() + + if self.cfg.enable_sequential_cpu_offload: + self.pipe.enable_sequential_cpu_offload() + + if self.cfg.enable_attention_slicing: + self.pipe.enable_attention_slicing(1) + + if self.cfg.enable_channels_last_format: + self.pipe.unet.to(memory_format=torch.channels_last) + + # Create model + self.vae = self.pipe.vae.eval() + self.unet = self.pipe.unet.eval() + + for p in self.vae.parameters(): + p.requires_grad_(False) + for p in self.unet.parameters(): + p.requires_grad_(False) + + self.num_train_timesteps = self.scheduler.config.num_train_timesteps + self.set_min_max_steps() # set to default value + + self.alphas: Float[Tensor, "..."] = self.scheduler.alphas_cumprod.to( + self.device + ) + + self.grad_clip_val: Optional[float] = None + + threestudio.info(f"Loaded InstructPix2Pix!") + + @torch.cuda.amp.autocast(enabled=False) + def set_min_max_steps(self, min_step_percent=0.02, max_step_percent=0.98): + self.min_step = int(self.num_train_timesteps * min_step_percent) + self.max_step = int(self.num_train_timesteps * max_step_percent) + + @torch.cuda.amp.autocast(enabled=False) + def forward_unet( + self, + latents: Float[Tensor, "..."], + t: Float[Tensor, "..."], + encoder_hidden_states: Float[Tensor, "..."], + ) -> Float[Tensor, "..."]: + input_dtype = latents.dtype + return self.unet( + latents.to(self.weights_dtype), + t.to(self.weights_dtype), + encoder_hidden_states=encoder_hidden_states.to(self.weights_dtype), + ).sample.to(input_dtype) + + @torch.cuda.amp.autocast(enabled=False) + def encode_images( + self, imgs: Float[Tensor, "B 3 H W"] + ) -> Float[Tensor, "B 4 DH DW"]: + input_dtype = imgs.dtype + imgs = imgs * 2.0 - 1.0 + posterior = self.vae.encode(imgs.to(self.weights_dtype)).latent_dist + latents = posterior.sample() * self.vae.config.scaling_factor + return latents.to(input_dtype) + + @torch.cuda.amp.autocast(enabled=False) + def encode_cond_images( + self, imgs: Float[Tensor, "B 3 H W"] + ) -> Float[Tensor, "B 4 DH DW"]: + input_dtype = imgs.dtype + imgs = imgs * 2.0 - 1.0 + posterior = self.vae.encode(imgs.to(self.weights_dtype)).latent_dist + latents = posterior.mode() + uncond_image_latents = torch.zeros_like(latents) + latents = torch.cat([latents, latents, uncond_image_latents], dim=0) + return latents.to(input_dtype) + + @torch.cuda.amp.autocast(enabled=False) + def decode_latents( + self, latents: Float[Tensor, "B 4 DH DW"] + ) -> Float[Tensor, "B 3 H W"]: + input_dtype = latents.dtype + latents = 1 / self.vae.config.scaling_factor * latents + image = self.vae.decode(latents.to(self.weights_dtype)).sample + image = (image * 0.5 + 0.5).clamp(0, 1) + return image.to(input_dtype) + + def edit_latents( + self, + text_embeddings: Float[Tensor, "BB 77 768"], + latents: Float[Tensor, "B 4 DH DW"], + image_cond_latents: Float[Tensor, "B 4 DH DW"], + t: Int[Tensor, "B"], + ) -> Float[Tensor, "B 4 DH DW"]: + self.scheduler.config.num_train_timesteps = t.item() + self.scheduler.set_timesteps(self.cfg.diffusion_steps) + with torch.no_grad(): + # add noise + noise = torch.randn_like(latents) + latents = self.scheduler.add_noise(latents, noise, t) # type: ignore + threestudio.debug("Start editing...") + # sections of code used from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py + for i, t in enumerate(self.scheduler.timesteps): + # predict the noise residual with unet, NO grad! + with torch.no_grad(): + # pred noise + latent_model_input = torch.cat([latents] * 3) + latent_model_input = torch.cat( + [latent_model_input, image_cond_latents], dim=1 + ) + + noise_pred = self.forward_unet( + latent_model_input, t, encoder_hidden_states=text_embeddings + ) + + # perform classifier-free guidance + noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred.chunk( + 3 + ) + noise_pred = ( + noise_pred_uncond + + self.cfg.guidance_scale * (noise_pred_text - noise_pred_image) + + self.cfg.condition_scale * (noise_pred_image - noise_pred_uncond) + ) + + # get previous sample, continue loop + latents = self.scheduler.step(noise_pred, t, latents).prev_sample + threestudio.debug("Editing finished.") + return latents + + def compute_grad_sds( + self, + text_embeddings: Float[Tensor, "BB 77 768"], + latents: Float[Tensor, "B 4 DH DW"], + image_cond_latents: Float[Tensor, "B 4 DH DW"], + t: Int[Tensor, "B"], + ): + with torch.no_grad(): + # add noise + noise = torch.randn_like(latents) # TODO: use torch generator + latents_noisy = self.scheduler.add_noise(latents, noise, t) + # pred noise + latent_model_input = torch.cat([latents_noisy] * 3) + latent_model_input = torch.cat( + [latent_model_input, image_cond_latents], dim=1 + ) + + noise_pred = self.forward_unet( + latent_model_input, t, encoder_hidden_states=text_embeddings + ) + + noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred.chunk(3) + noise_pred = ( + noise_pred_uncond + + self.cfg.guidance_scale * (noise_pred_text - noise_pred_image) + + self.cfg.condition_scale * (noise_pred_image - noise_pred_uncond) + ) + + w = (1 - self.alphas[t]).view(-1, 1, 1, 1) + grad = w * (noise_pred - noise) + return grad + + def __call__( + self, + rgb: Float[Tensor, "B H W C"], + cond_rgb: Float[Tensor, "B H W C"], + prompt_utils: PromptProcessorOutput, + **kwargs, + ): + batch_size, H, W, _ = rgb.shape + + rgb_BCHW = rgb.permute(0, 3, 1, 2) + latents: Float[Tensor, "B 4 DH DW"] + if self.cfg.fixed_size > 0: + RH, RW = self.cfg.fixed_size, self.cfg.fixed_size + else: + RH, RW = H // 8 * 8, W // 8 * 8 + rgb_BCHW_HW8 = F.interpolate( + rgb_BCHW, (RH, RW), mode="bilinear", align_corners=False + ) + latents = self.encode_images(rgb_BCHW_HW8) + + cond_rgb_BCHW = cond_rgb.permute(0, 3, 1, 2) + cond_rgb_BCHW_HW8 = F.interpolate( + cond_rgb_BCHW, + (RH, RW), + mode="bilinear", + align_corners=False, + ) + cond_latents = self.encode_cond_images(cond_rgb_BCHW_HW8) + + temp = torch.zeros(1).to(rgb.device) + text_embeddings = prompt_utils.get_text_embeddings(temp, temp, temp, False) + text_embeddings = torch.cat( + [text_embeddings, text_embeddings[-1:]], dim=0 + ) # [positive, negative, negative] + + # timestep ~ U(0.02, 0.98) to avoid very high/low noise level + t = torch.randint( + self.min_step, + self.max_step + 1, + [batch_size], + dtype=torch.long, + device=self.device, + ) + + if self.cfg.use_sds: + grad = self.compute_grad_sds(text_embeddings, latents, cond_latents, t) + grad = torch.nan_to_num(grad) + if self.grad_clip_val is not None: + grad = grad.clamp(-self.grad_clip_val, self.grad_clip_val) + target = (latents - grad).detach() + loss_sds = 0.5 * F.mse_loss(latents, target, reduction="sum") / batch_size + return { + "loss_sds": loss_sds, + "grad_norm": grad.norm(), + "min_step": self.min_step, + "max_step": self.max_step, + } + else: + edit_latents = self.edit_latents(text_embeddings, latents, cond_latents, t) + edit_images = self.decode_latents(edit_latents) + edit_images = F.interpolate(edit_images, (H, W), mode="bilinear") + + return {"edit_images": edit_images.permute(0, 2, 3, 1)} + + def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False): + # clip grad for stable training as demonstrated in + # Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation + # http://arxiv.org/abs/2303.15413 + if self.cfg.grad_clip is not None: + self.grad_clip_val = C(self.cfg.grad_clip, epoch, global_step) + + self.set_min_max_steps( + min_step_percent=C(self.cfg.min_step_percent, epoch, global_step), + max_step_percent=C(self.cfg.max_step_percent, epoch, global_step), + ) + + +if __name__ == "__main__": + from threestudio.utils.config import ExperimentConfig, load_config + from threestudio.utils.typing import Optional + + cfg = load_config("configs/debugging/instructpix2pix.yaml") + guidance = threestudio.find(cfg.system.guidance_type)(cfg.system.guidance) + prompt_processor = threestudio.find(cfg.system.prompt_processor_type)( + cfg.system.prompt_processor + ) + rgb_image = cv2.imread("assets/face.jpg")[:, :, ::-1].copy() / 255 + rgb_image = torch.FloatTensor(rgb_image).unsqueeze(0).to(guidance.device) + prompt_utils = prompt_processor() + guidance_out = guidance(rgb_image, rgb_image, prompt_utils) + edit_image = ( + ( + guidance_out["edit_images"][0] + .permute(1, 2, 0) + .detach() + .cpu() + .clip(0, 1) + .numpy() + * 255 + ) + .astype(np.uint8)[:, :, ::-1] + .copy() + ) + import os + + os.makedirs(".threestudio_cache", exist_ok=True) + cv2.imwrite(".threestudio_cache/edit_image.jpg", edit_image) diff --git a/threestudio/models/guidance/stable_diffusion_guidance.py b/threestudio/models/guidance/stable_diffusion_guidance.py new file mode 100644 index 0000000..c5c91bd --- /dev/null +++ b/threestudio/models/guidance/stable_diffusion_guidance.py @@ -0,0 +1,637 @@ +from dataclasses import dataclass, field + +import torch +import torch.nn as nn +import torch.nn.functional as F +from diffusers import DDIMScheduler, DDPMScheduler, StableDiffusionPipeline +from diffusers.utils.import_utils import is_xformers_available +from tqdm import tqdm + +import threestudio +from threestudio.models.prompt_processors.base import PromptProcessorOutput +from threestudio.utils.base import BaseObject +from threestudio.utils.misc import C, cleanup, parse_version +from threestudio.utils.ops import perpendicular_component +from threestudio.utils.typing import * + + +@threestudio.register("stable-diffusion-guidance") +class StableDiffusionGuidance(BaseObject): + @dataclass + class Config(BaseObject.Config): + pretrained_model_name_or_path: str = "runwayml/stable-diffusion-v1-5" + enable_memory_efficient_attention: bool = False + enable_sequential_cpu_offload: bool = False + enable_attention_slicing: bool = False + enable_channels_last_format: bool = False + guidance_scale: float = 100.0 + grad_clip: Optional[ + Any + ] = None # field(default_factory=lambda: [0, 2.0, 8.0, 1000]) + half_precision_weights: bool = True + + min_step_percent: float = 0.02 + max_step_percent: float = 0.98 + sqrt_anneal: bool = False # sqrt anneal proposed in HiFA: https://hifa-team.github.io/HiFA-site/ + trainer_max_steps: int = 25000 + use_img_loss: bool = False # image-space SDS proposed in HiFA: https://hifa-team.github.io/HiFA-site/ + + use_sjc: bool = False + var_red: bool = True + weighting_strategy: str = "sds" + + token_merging: bool = False + token_merging_params: Optional[dict] = field(default_factory=dict) + + view_dependent_prompting: bool = True + + """Maximum number of batch items to evaluate guidance for (for debugging) and to save on disk. -1 means save all items.""" + max_items_eval: int = 4 + + cfg: Config + + def configure(self) -> None: + threestudio.info(f"Loading Stable Diffusion ...") + + self.weights_dtype = ( + torch.float16 if self.cfg.half_precision_weights else torch.float32 + ) + + pipe_kwargs = { + "tokenizer": None, + "safety_checker": None, + "feature_extractor": None, + "requires_safety_checker": False, + "torch_dtype": self.weights_dtype, + } + self.pipe = StableDiffusionPipeline.from_pretrained( + self.cfg.pretrained_model_name_or_path, + **pipe_kwargs, + ).to(self.device) + + if self.cfg.enable_memory_efficient_attention: + if parse_version(torch.__version__) >= parse_version("2"): + threestudio.info( + "PyTorch2.0 uses memory efficient attention by default." + ) + elif not is_xformers_available(): + threestudio.warn( + "xformers is not available, memory efficient attention is not enabled." + ) + else: + self.pipe.enable_xformers_memory_efficient_attention() + + if self.cfg.enable_sequential_cpu_offload: + self.pipe.enable_sequential_cpu_offload() + + if self.cfg.enable_attention_slicing: + self.pipe.enable_attention_slicing(1) + + if self.cfg.enable_channels_last_format: + self.pipe.unet.to(memory_format=torch.channels_last) + + del self.pipe.text_encoder + cleanup() + + # Create model + self.vae = self.pipe.vae.eval() + self.unet = self.pipe.unet.eval() + + for p in self.vae.parameters(): + p.requires_grad_(False) + for p in self.unet.parameters(): + p.requires_grad_(False) + + if self.cfg.token_merging: + import tomesd + + tomesd.apply_patch(self.unet, **self.cfg.token_merging_params) + + if self.cfg.use_sjc: + # score jacobian chaining use DDPM + self.scheduler = DDPMScheduler.from_pretrained( + self.cfg.pretrained_model_name_or_path, + subfolder="scheduler", + torch_dtype=self.weights_dtype, + beta_start=0.00085, + beta_end=0.0120, + beta_schedule="scaled_linear", + ) + else: + self.scheduler = DDIMScheduler.from_pretrained( + self.cfg.pretrained_model_name_or_path, + subfolder="scheduler", + torch_dtype=self.weights_dtype, + ) + + self.num_train_timesteps = self.scheduler.config.num_train_timesteps + self.set_min_max_steps() # set to default value + + self.alphas: Float[Tensor, "..."] = self.scheduler.alphas_cumprod.to( + self.device + ) + if self.cfg.use_sjc: + # score jacobian chaining need mu + self.us: Float[Tensor, "..."] = torch.sqrt((1 - self.alphas) / self.alphas) + + self.grad_clip_val: Optional[float] = None + + threestudio.info(f"Loaded Stable Diffusion!") + + @torch.cuda.amp.autocast(enabled=False) + def set_min_max_steps(self, min_step_percent=0.02, max_step_percent=0.98): + self.min_step = int(self.num_train_timesteps * min_step_percent) + self.max_step = int(self.num_train_timesteps * max_step_percent) + + @torch.cuda.amp.autocast(enabled=False) + def forward_unet( + self, + latents: Float[Tensor, "..."], + t: Float[Tensor, "..."], + encoder_hidden_states: Float[Tensor, "..."], + ) -> Float[Tensor, "..."]: + input_dtype = latents.dtype + return self.unet( + latents.to(self.weights_dtype), + t.to(self.weights_dtype), + encoder_hidden_states=encoder_hidden_states.to(self.weights_dtype), + ).sample.to(input_dtype) + + @torch.cuda.amp.autocast(enabled=False) + def encode_images( + self, imgs: Float[Tensor, "B 3 512 512"] + ) -> Float[Tensor, "B 4 64 64"]: + input_dtype = imgs.dtype + imgs = imgs * 2.0 - 1.0 + posterior = self.vae.encode(imgs.to(self.weights_dtype)).latent_dist + latents = posterior.sample() * self.vae.config.scaling_factor + return latents.to(input_dtype) + + @torch.cuda.amp.autocast(enabled=False) + def decode_latents( + self, + latents: Float[Tensor, "B 4 H W"], + latent_height: int = 64, + latent_width: int = 64, + ) -> Float[Tensor, "B 3 512 512"]: + input_dtype = latents.dtype + latents = F.interpolate( + latents, (latent_height, latent_width), mode="bilinear", align_corners=False + ) + latents = 1 / self.vae.config.scaling_factor * latents + image = self.vae.decode(latents.to(self.weights_dtype)).sample + image = (image * 0.5 + 0.5).clamp(0, 1) + return image.to(input_dtype) + + def compute_grad_sds( + self, + latents: Float[Tensor, "B 4 64 64"], + image: Float[Tensor, "B 3 512 512"], + t: Int[Tensor, "B"], + prompt_utils: PromptProcessorOutput, + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + ): + batch_size = elevation.shape[0] + + if prompt_utils.use_perp_neg: + ( + text_embeddings, + neg_guidance_weights, + ) = prompt_utils.get_text_embeddings_perp_neg( + elevation, azimuth, camera_distances, self.cfg.view_dependent_prompting + ) + with torch.no_grad(): + noise = torch.randn_like(latents) + latents_noisy = self.scheduler.add_noise(latents, noise, t) + latent_model_input = torch.cat([latents_noisy] * 4, dim=0) + noise_pred = self.forward_unet( + latent_model_input, + torch.cat([t] * 4), + encoder_hidden_states=text_embeddings, + ) # (4B, 3, 64, 64) + + noise_pred_text = noise_pred[:batch_size] + noise_pred_uncond = noise_pred[batch_size : batch_size * 2] + noise_pred_neg = noise_pred[batch_size * 2 :] + + e_pos = noise_pred_text - noise_pred_uncond + accum_grad = 0 + n_negative_prompts = neg_guidance_weights.shape[-1] + for i in range(n_negative_prompts): + e_i_neg = noise_pred_neg[i::n_negative_prompts] - noise_pred_uncond + accum_grad += neg_guidance_weights[:, i].view( + -1, 1, 1, 1 + ) * perpendicular_component(e_i_neg, e_pos) + + noise_pred = noise_pred_uncond + self.cfg.guidance_scale * ( + e_pos + accum_grad + ) + else: + neg_guidance_weights = None + text_embeddings = prompt_utils.get_text_embeddings( + elevation, azimuth, camera_distances, self.cfg.view_dependent_prompting + ) + # predict the noise residual with unet, NO grad! + with torch.no_grad(): + # add noise + noise = torch.randn_like(latents) # TODO: use torch generator + latents_noisy = self.scheduler.add_noise(latents, noise, t) + # pred noise + latent_model_input = torch.cat([latents_noisy] * 2, dim=0) + noise_pred = self.forward_unet( + latent_model_input, + torch.cat([t] * 2), + encoder_hidden_states=text_embeddings, + ) + + # perform guidance (high scale from paper!) + noise_pred_text, noise_pred_uncond = noise_pred.chunk(2) + noise_pred = noise_pred_text + self.cfg.guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + + if self.cfg.weighting_strategy == "sds": + # w(t), sigma_t^2 + w = (1 - self.alphas[t]).view(-1, 1, 1, 1) + elif self.cfg.weighting_strategy == "uniform": + w = 1 + elif self.cfg.weighting_strategy == "fantasia3d": + w = (self.alphas[t] ** 0.5 * (1 - self.alphas[t])).view(-1, 1, 1, 1) + else: + raise ValueError( + f"Unknown weighting strategy: {self.cfg.weighting_strategy}" + ) + + alpha = (self.alphas[t] ** 0.5).view(-1, 1, 1, 1) + sigma = ((1 - self.alphas[t]) ** 0.5).view(-1, 1, 1, 1) + latents_denoised = (latents_noisy - sigma * noise_pred) / alpha + image_denoised = self.decode_latents(latents_denoised) + + grad = w * (noise_pred - noise) + # image-space SDS proposed in HiFA: https://hifa-team.github.io/HiFA-site/ + if self.cfg.use_img_loss: + grad_img = w * (image - image_denoised) * alpha / sigma + else: + grad_img = None + + guidance_eval_utils = { + "use_perp_neg": prompt_utils.use_perp_neg, + "neg_guidance_weights": neg_guidance_weights, + "text_embeddings": text_embeddings, + "t_orig": t, + "latents_noisy": latents_noisy, + "noise_pred": noise_pred, + } + + return grad, grad_img, guidance_eval_utils + + def compute_grad_sjc( + self, + latents: Float[Tensor, "B 4 64 64"], + t: Int[Tensor, "B"], + prompt_utils: PromptProcessorOutput, + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + ): + batch_size = elevation.shape[0] + + sigma = self.us[t] + sigma = sigma.view(-1, 1, 1, 1) + + if prompt_utils.use_perp_neg: + ( + text_embeddings, + neg_guidance_weights, + ) = prompt_utils.get_text_embeddings_perp_neg( + elevation, azimuth, camera_distances, self.cfg.view_dependent_prompting + ) + with torch.no_grad(): + noise = torch.randn_like(latents) + y = latents + zs = y + sigma * noise + scaled_zs = zs / torch.sqrt(1 + sigma**2) + # pred noise + latent_model_input = torch.cat([scaled_zs] * 4, dim=0) + noise_pred = self.forward_unet( + latent_model_input, + torch.cat([t] * 4), + encoder_hidden_states=text_embeddings, + ) # (4B, 3, 64, 64) + + noise_pred_text = noise_pred[:batch_size] + noise_pred_uncond = noise_pred[batch_size : batch_size * 2] + noise_pred_neg = noise_pred[batch_size * 2 :] + + e_pos = noise_pred_text - noise_pred_uncond + accum_grad = 0 + n_negative_prompts = neg_guidance_weights.shape[-1] + for i in range(n_negative_prompts): + e_i_neg = noise_pred_neg[i::n_negative_prompts] - noise_pred_uncond + accum_grad += neg_guidance_weights[:, i].view( + -1, 1, 1, 1 + ) * perpendicular_component(e_i_neg, e_pos) + + noise_pred = noise_pred_uncond + self.cfg.guidance_scale * ( + e_pos + accum_grad + ) + else: + neg_guidance_weights = None + text_embeddings = prompt_utils.get_text_embeddings( + elevation, azimuth, camera_distances, self.cfg.view_dependent_prompting + ) + # predict the noise residual with unet, NO grad! + with torch.no_grad(): + # add noise + noise = torch.randn_like(latents) # TODO: use torch generator + y = latents + + zs = y + sigma * noise + scaled_zs = zs / torch.sqrt(1 + sigma**2) + + # pred noise + latent_model_input = torch.cat([scaled_zs] * 2, dim=0) + noise_pred = self.forward_unet( + latent_model_input, + torch.cat([t] * 2), + encoder_hidden_states=text_embeddings, + ) + + # perform guidance (high scale from paper!) + noise_pred_text, noise_pred_uncond = noise_pred.chunk(2) + noise_pred = noise_pred_text + self.cfg.guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + + Ds = zs - sigma * noise_pred + + if self.cfg.var_red: + grad = -(Ds - y) / sigma + else: + grad = -(Ds - zs) / sigma + + guidance_eval_utils = { + "use_perp_neg": prompt_utils.use_perp_neg, + "neg_guidance_weights": neg_guidance_weights, + "text_embeddings": text_embeddings, + "t_orig": t, + "latents_noisy": scaled_zs, + "noise_pred": noise_pred, + } + + return grad, guidance_eval_utils + + def __call__( + self, + rgb: Float[Tensor, "B H W C"], + prompt_utils: PromptProcessorOutput, + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + rgb_as_latents=False, + guidance_eval=False, + **kwargs, + ): + batch_size = rgb.shape[0] + + rgb_BCHW = rgb.permute(0, 3, 1, 2) + latents: Float[Tensor, "B 4 64 64"] + rgb_BCHW_512 = F.interpolate( + rgb_BCHW, (512, 512), mode="bilinear", align_corners=False + ) + if rgb_as_latents: + latents = F.interpolate( + rgb_BCHW, (64, 64), mode="bilinear", align_corners=False + ) + else: + # encode image into latents with vae + latents = self.encode_images(rgb_BCHW_512) + + # timestep ~ U(0.02, 0.98) to avoid very high/low noise level + t = torch.randint( + self.min_step, + self.max_step + 1, + [batch_size], + dtype=torch.long, + device=self.device, + ) + + if self.cfg.use_sjc: + grad, guidance_eval_utils = self.compute_grad_sjc( + latents, t, prompt_utils, elevation, azimuth, camera_distances + ) + grad_img = torch.tensor([0.0], dtype=grad.dtype).to(grad.device) + else: + grad, grad_img, guidance_eval_utils = self.compute_grad_sds( + latents, + rgb_BCHW_512, + t, + prompt_utils, + elevation, + azimuth, + camera_distances, + ) + + grad = torch.nan_to_num(grad) + + # clip grad for stable training? + if self.grad_clip_val is not None: + grad = grad.clamp(-self.grad_clip_val, self.grad_clip_val) + + # loss = SpecifyGradient.apply(latents, grad) + # SpecifyGradient is not straghtforward, use a reparameterization trick instead + target = (latents - grad).detach() + # d(loss)/d(latents) = latents - target = latents - (latents - grad) = grad + loss_sds = 0.5 * F.mse_loss(latents, target, reduction="sum") / batch_size + + guidance_out = { + "loss_sds": loss_sds, + "grad_norm": grad.norm(), + "min_step": self.min_step, + "max_step": self.max_step, + } + + if self.cfg.use_img_loss: + grad_img = torch.nan_to_num(grad_img) + if self.grad_clip_val is not None: + grad_img = grad_img.clamp(-self.grad_clip_val, self.grad_clip_val) + target_img = (rgb_BCHW_512 - grad_img).detach() + loss_sds_img = ( + 0.5 * F.mse_loss(rgb_BCHW_512, target_img, reduction="sum") / batch_size + ) + guidance_out["loss_sds_img"] = loss_sds_img + + if guidance_eval: + guidance_eval_out = self.guidance_eval(**guidance_eval_utils) + texts = [] + for n, e, a, c in zip( + guidance_eval_out["noise_levels"], elevation, azimuth, camera_distances + ): + texts.append( + f"n{n:.02f}\ne{e.item():.01f}\na{a.item():.01f}\nc{c.item():.02f}" + ) + guidance_eval_out.update({"texts": texts}) + guidance_out.update({"eval": guidance_eval_out}) + + return guidance_out + + @torch.cuda.amp.autocast(enabled=False) + @torch.no_grad() + def get_noise_pred( + self, + latents_noisy, + t, + text_embeddings, + use_perp_neg=False, + neg_guidance_weights=None, + ): + batch_size = latents_noisy.shape[0] + + if use_perp_neg: + # pred noise + latent_model_input = torch.cat([latents_noisy] * 4, dim=0) + noise_pred = self.forward_unet( + latent_model_input, + torch.cat([t.reshape(1)] * 4).to(self.device), + encoder_hidden_states=text_embeddings, + ) # (4B, 3, 64, 64) + + noise_pred_text = noise_pred[:batch_size] + noise_pred_uncond = noise_pred[batch_size : batch_size * 2] + noise_pred_neg = noise_pred[batch_size * 2 :] + + e_pos = noise_pred_text - noise_pred_uncond + accum_grad = 0 + n_negative_prompts = neg_guidance_weights.shape[-1] + for i in range(n_negative_prompts): + e_i_neg = noise_pred_neg[i::n_negative_prompts] - noise_pred_uncond + accum_grad += neg_guidance_weights[:, i].view( + -1, 1, 1, 1 + ) * perpendicular_component(e_i_neg, e_pos) + + noise_pred = noise_pred_uncond + self.cfg.guidance_scale * ( + e_pos + accum_grad + ) + else: + # pred noise + latent_model_input = torch.cat([latents_noisy] * 2, dim=0) + noise_pred = self.forward_unet( + latent_model_input, + torch.cat([t.reshape(1)] * 2).to(self.device), + encoder_hidden_states=text_embeddings, + ) + # perform guidance (high scale from paper!) + noise_pred_text, noise_pred_uncond = noise_pred.chunk(2) + noise_pred = noise_pred_text + self.cfg.guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + + return noise_pred + + @torch.cuda.amp.autocast(enabled=False) + @torch.no_grad() + def guidance_eval( + self, + t_orig, + text_embeddings, + latents_noisy, + noise_pred, + use_perp_neg=False, + neg_guidance_weights=None, + ): + # use only 50 timesteps, and find nearest of those to t + self.scheduler.set_timesteps(50) + self.scheduler.timesteps_gpu = self.scheduler.timesteps.to(self.device) + bs = ( + min(self.cfg.max_items_eval, latents_noisy.shape[0]) + if self.cfg.max_items_eval > 0 + else latents_noisy.shape[0] + ) # batch size + large_enough_idxs = self.scheduler.timesteps_gpu.expand([bs, -1]) > t_orig[ + :bs + ].unsqueeze( + -1 + ) # sized [bs,50] > [bs,1] + idxs = torch.min(large_enough_idxs, dim=1)[1] + t = self.scheduler.timesteps_gpu[idxs] + + fracs = list((t / self.scheduler.config.num_train_timesteps).cpu().numpy()) + imgs_noisy = self.decode_latents(latents_noisy[:bs]).permute(0, 2, 3, 1) + + # get prev latent + latents_1step = [] + pred_1orig = [] + for b in range(bs): + step_output = self.scheduler.step( + noise_pred[b : b + 1], t[b], latents_noisy[b : b + 1], eta=1 + ) + latents_1step.append(step_output["prev_sample"]) + pred_1orig.append(step_output["pred_original_sample"]) + latents_1step = torch.cat(latents_1step) + pred_1orig = torch.cat(pred_1orig) + imgs_1step = self.decode_latents(latents_1step).permute(0, 2, 3, 1) + imgs_1orig = self.decode_latents(pred_1orig).permute(0, 2, 3, 1) + + latents_final = [] + for b, i in enumerate(idxs): + latents = latents_1step[b : b + 1] + text_emb = ( + text_embeddings[ + [b, b + len(idxs), b + 2 * len(idxs), b + 3 * len(idxs)], ... + ] + if use_perp_neg + else text_embeddings[[b, b + len(idxs)], ...] + ) + neg_guid = neg_guidance_weights[b : b + 1] if use_perp_neg else None + for t in tqdm(self.scheduler.timesteps[i + 1 :], leave=False): + # pred noise + noise_pred = self.get_noise_pred( + latents, t, text_emb, use_perp_neg, neg_guid + ) + # get prev latent + latents = self.scheduler.step(noise_pred, t, latents, eta=1)[ + "prev_sample" + ] + latents_final.append(latents) + + latents_final = torch.cat(latents_final) + imgs_final = self.decode_latents(latents_final).permute(0, 2, 3, 1) + + return { + "bs": bs, + "noise_levels": fracs, + "imgs_noisy": imgs_noisy, + "imgs_1step": imgs_1step, + "imgs_1orig": imgs_1orig, + "imgs_final": imgs_final, + } + + def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False): + # clip grad for stable training as demonstrated in + # Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation + # http://arxiv.org/abs/2303.15413 + if self.cfg.grad_clip is not None: + self.grad_clip_val = C(self.cfg.grad_clip, epoch, global_step) + + if self.cfg.sqrt_anneal: + percentage = ( + float(global_step) / self.cfg.trainer_max_steps + ) ** 0.5 # progress percentage + if type(self.cfg.max_step_percent) not in [float, int]: + max_step_percent = self.cfg.max_step_percent[1] + else: + max_step_percent = self.cfg.max_step_percent + curr_percent = ( + max_step_percent - C(self.cfg.min_step_percent, epoch, global_step) + ) * (1 - percentage) + C(self.cfg.min_step_percent, epoch, global_step) + self.set_min_max_steps( + min_step_percent=curr_percent, + max_step_percent=curr_percent, + ) + else: + self.set_min_max_steps( + min_step_percent=C(self.cfg.min_step_percent, epoch, global_step), + max_step_percent=C(self.cfg.max_step_percent, epoch, global_step), + ) diff --git a/threestudio/models/guidance/stable_diffusion_unified_guidance.py b/threestudio/models/guidance/stable_diffusion_unified_guidance.py new file mode 100644 index 0000000..5774b1d --- /dev/null +++ b/threestudio/models/guidance/stable_diffusion_unified_guidance.py @@ -0,0 +1,779 @@ +import random +from contextlib import contextmanager +from dataclasses import dataclass, field + +import torch +import torch.nn as nn +import torch.nn.functional as F +from diffusers import ( + AutoencoderKL, + ControlNetModel, + DDPMScheduler, + DPMSolverSinglestepScheduler, + StableDiffusionPipeline, + UNet2DConditionModel, +) +from diffusers.loaders import AttnProcsLayers +from diffusers.models.attention_processor import LoRAAttnProcessor +from diffusers.models.embeddings import TimestepEmbedding +from diffusers.utils.import_utils import is_xformers_available +from tqdm import tqdm + +import threestudio +from threestudio.models.networks import ToDTypeWrapper +from threestudio.models.prompt_processors.base import PromptProcessorOutput +from threestudio.utils.base import BaseModule +from threestudio.utils.misc import C, cleanup, enable_gradient, parse_version +from threestudio.utils.ops import perpendicular_component +from threestudio.utils.typing import * + + +@threestudio.register("stable-diffusion-unified-guidance") +class StableDiffusionUnifiedGuidance(BaseModule): + @dataclass + class Config(BaseModule.Config): + # guidance type, in ["sds", "vsd"] + guidance_type: str = "sds" + + pretrained_model_name_or_path: str = "runwayml/stable-diffusion-v1-5" + guidance_scale: float = 100.0 + weighting_strategy: str = "dreamfusion" + view_dependent_prompting: bool = True + + min_step_percent: Any = 0.02 + max_step_percent: Any = 0.98 + grad_clip: Optional[Any] = None + + return_rgb_1step_orig: bool = False + return_rgb_multistep_orig: bool = False + n_rgb_multistep_orig_steps: int = 4 + + # TODO + # controlnet + controlnet_model_name_or_path: Optional[str] = None + preprocessor: Optional[str] = None + control_scale: float = 1.0 + + # TODO + # lora + lora_model_name_or_path: Optional[str] = None + + # efficiency-related configurations + half_precision_weights: bool = True + enable_memory_efficient_attention: bool = False + enable_sequential_cpu_offload: bool = False + enable_attention_slicing: bool = False + enable_channels_last_format: bool = False + token_merging: bool = False + token_merging_params: Optional[dict] = field(default_factory=dict) + + # VSD configurations, only used when guidance_type is "vsd" + vsd_phi_model_name_or_path: Optional[str] = None + vsd_guidance_scale_phi: float = 1.0 + vsd_use_lora: bool = True + vsd_lora_cfg_training: bool = False + vsd_lora_n_timestamp_samples: int = 1 + vsd_use_camera_condition: bool = True + # camera condition type, in ["extrinsics", "mvp", "spherical"] + vsd_camera_condition_type: Optional[str] = "extrinsics" + + # HiFA configurations: https://hifa-team.github.io/HiFA-site/ + sqrt_anneal: bool = ( + False # requires setting min_step_percent=0.3 to work properly + ) + trainer_max_steps: int = 25000 + use_img_loss: bool = True # works with most cases + + cfg: Config + + def configure(self) -> None: + self.min_step: Optional[int] = None + self.max_step: Optional[int] = None + self.grad_clip_val: Optional[float] = None + + @dataclass + class NonTrainableModules: + pipe: StableDiffusionPipeline + pipe_phi: Optional[StableDiffusionPipeline] = None + controlnet: Optional[ControlNetModel] = None + + self.weights_dtype = ( + torch.float16 if self.cfg.half_precision_weights else torch.float32 + ) + + threestudio.info(f"Loading Stable Diffusion ...") + + pipe_kwargs = { + "tokenizer": None, + "safety_checker": None, + "feature_extractor": None, + "requires_safety_checker": False, + "torch_dtype": self.weights_dtype, + } + pipe = StableDiffusionPipeline.from_pretrained( + self.cfg.pretrained_model_name_or_path, + **pipe_kwargs, + ).to(self.device) + self.prepare_pipe(pipe) + self.configure_pipe_token_merging(pipe) + + # phi network for VSD + # introduce two trainable modules: + # - self.camera_embedding + # - self.lora_layers + pipe_phi = None + + # if the phi network shares the same unet with the pretrain network + # we need to pass additional cross attention kwargs to the unet + self.vsd_share_model = ( + self.cfg.guidance_type == "vsd" + and self.cfg.vsd_phi_model_name_or_path is None + ) + if self.cfg.guidance_type == "vsd": + if self.cfg.vsd_phi_model_name_or_path is None: + pipe_phi = pipe + else: + pipe_phi = StableDiffusionPipeline.from_pretrained( + self.cfg.vsd_phi_model_name_or_path, + **pipe_kwargs, + ).to(self.device) + self.prepare_pipe(pipe_phi) + self.configure_pipe_token_merging(pipe_phi) + + # set up camera embedding + if self.cfg.vsd_use_camera_condition: + if self.cfg.vsd_camera_condition_type in ["extrinsics", "mvp"]: + self.camera_embedding_dim = 16 + elif self.cfg.vsd_camera_condition_type == "spherical": + self.camera_embedding_dim = 4 + else: + raise ValueError("Invalid camera condition type!") + + # FIXME: hard-coded output dim + self.camera_embedding = ToDTypeWrapper( + TimestepEmbedding(self.camera_embedding_dim, 1280), + self.weights_dtype, + ).to(self.device) + pipe_phi.unet.class_embedding = self.camera_embedding + + if self.cfg.vsd_use_lora: + # set up LoRA layers + lora_attn_procs = {} + for name in pipe_phi.unet.attn_processors.keys(): + cross_attention_dim = ( + None + if name.endswith("attn1.processor") + else pipe_phi.unet.config.cross_attention_dim + ) + if name.startswith("mid_block"): + hidden_size = pipe_phi.unet.config.block_out_channels[-1] + elif name.startswith("up_blocks"): + block_id = int(name[len("up_blocks.")]) + hidden_size = list( + reversed(pipe_phi.unet.config.block_out_channels) + )[block_id] + elif name.startswith("down_blocks"): + block_id = int(name[len("down_blocks.")]) + hidden_size = pipe_phi.unet.config.block_out_channels[block_id] + + lora_attn_procs[name] = LoRAAttnProcessor( + hidden_size=hidden_size, cross_attention_dim=cross_attention_dim + ) + + pipe_phi.unet.set_attn_processor(lora_attn_procs) + + self.lora_layers = AttnProcsLayers(pipe_phi.unet.attn_processors).to( + self.device + ) + self.lora_layers._load_state_dict_pre_hooks.clear() + self.lora_layers._state_dict_hooks.clear() + + threestudio.info(f"Loaded Stable Diffusion!") + + # controlnet + controlnet = None + if self.cfg.controlnet_model_name_or_path is not None: + threestudio.info(f"Loading ControlNet ...") + + controlnet = ControlNetModel.from_pretrained( + self.cfg.controlnet_model_name_or_path, + torch_dtype=self.weights_dtype, + ).to(self.device) + controlnet.eval() + enable_gradient(controlnet, enabled=False) + + threestudio.info(f"Loaded ControlNet!") + + self.scheduler = DDPMScheduler.from_config(pipe.scheduler.config) + self.num_train_timesteps = self.scheduler.config.num_train_timesteps + + # q(z_t|x) = N(alpha_t x, sigma_t^2 I) + # in DDPM, alpha_t = sqrt(alphas_cumprod_t), sigma_t^2 = 1 - alphas_cumprod_t + self.alphas_cumprod: Float[Tensor, "T"] = self.scheduler.alphas_cumprod.to( + self.device + ) + self.alphas: Float[Tensor, "T"] = self.alphas_cumprod**0.5 + self.sigmas: Float[Tensor, "T"] = (1 - self.alphas_cumprod) ** 0.5 + # log SNR + self.lambdas: Float[Tensor, "T"] = self.sigmas / self.alphas + + self._non_trainable_modules = NonTrainableModules( + pipe=pipe, + pipe_phi=pipe_phi, + controlnet=controlnet, + ) + + @property + def pipe(self) -> StableDiffusionPipeline: + return self._non_trainable_modules.pipe + + @property + def pipe_phi(self) -> StableDiffusionPipeline: + if self._non_trainable_modules.pipe_phi is None: + raise RuntimeError("phi model is not available.") + return self._non_trainable_modules.pipe_phi + + @property + def controlnet(self) -> ControlNetModel: + if self._non_trainable_modules.controlnet is None: + raise RuntimeError("ControlNet model is not available.") + return self._non_trainable_modules.controlnet + + def prepare_pipe(self, pipe: StableDiffusionPipeline): + if self.cfg.enable_memory_efficient_attention: + if parse_version(torch.__version__) >= parse_version("2"): + threestudio.info( + "PyTorch2.0 uses memory efficient attention by default." + ) + elif not is_xformers_available(): + threestudio.warn( + "xformers is not available, memory efficient attention is not enabled." + ) + else: + pipe.enable_xformers_memory_efficient_attention() + + if self.cfg.enable_sequential_cpu_offload: + pipe.enable_sequential_cpu_offload() + + if self.cfg.enable_attention_slicing: + pipe.enable_attention_slicing(1) + + if self.cfg.enable_channels_last_format: + pipe.unet.to(memory_format=torch.channels_last) + + # FIXME: pipe.__call__ requires text_encoder.dtype + # pipe.text_encoder.to("meta") + cleanup() + + pipe.vae.eval() + pipe.unet.eval() + + enable_gradient(pipe.vae, enabled=False) + enable_gradient(pipe.unet, enabled=False) + + # disable progress bar + pipe.set_progress_bar_config(disable=True) + + def configure_pipe_token_merging(self, pipe: StableDiffusionPipeline): + if self.cfg.token_merging: + import tomesd + + tomesd.apply_patch(pipe.unet, **self.cfg.token_merging_params) + + @torch.cuda.amp.autocast(enabled=False) + def forward_unet( + self, + unet: UNet2DConditionModel, + latents: Float[Tensor, "..."], + t: Int[Tensor, "..."], + encoder_hidden_states: Float[Tensor, "..."], + class_labels: Optional[Float[Tensor, "..."]] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + down_block_additional_residuals: Optional[Float[Tensor, "..."]] = None, + mid_block_additional_residual: Optional[Float[Tensor, "..."]] = None, + velocity_to_epsilon: bool = False, + ) -> Float[Tensor, "..."]: + input_dtype = latents.dtype + pred = unet( + latents.to(unet.dtype), + t.to(unet.dtype), + encoder_hidden_states=encoder_hidden_states.to(unet.dtype), + class_labels=class_labels, + cross_attention_kwargs=cross_attention_kwargs, + down_block_additional_residuals=down_block_additional_residuals, + mid_block_additional_residual=mid_block_additional_residual, + ).sample + if velocity_to_epsilon: + pred = latents * self.sigmas[t].view(-1, 1, 1, 1) + pred * self.alphas[ + t + ].view(-1, 1, 1, 1) + return pred.to(input_dtype) + + @torch.cuda.amp.autocast(enabled=False) + def vae_encode( + self, vae: AutoencoderKL, imgs: Float[Tensor, "B 3 H W"], mode=False + ) -> Float[Tensor, "B 4 Hl Wl"]: + # expect input in [-1, 1] + input_dtype = imgs.dtype + posterior = vae.encode(imgs.to(vae.dtype)).latent_dist + if mode: + latents = posterior.mode() + else: + latents = posterior.sample() + latents = latents * vae.config.scaling_factor + return latents.to(input_dtype) + + @torch.cuda.amp.autocast(enabled=False) + def vae_decode( + self, vae: AutoencoderKL, latents: Float[Tensor, "B 4 Hl Wl"] + ) -> Float[Tensor, "B 3 H W"]: + # output in [0, 1] + input_dtype = latents.dtype + latents = 1 / vae.config.scaling_factor * latents + image = vae.decode(latents.to(vae.dtype)).sample + image = (image * 0.5 + 0.5).clamp(0, 1) + return image.to(input_dtype) + + @contextmanager + def disable_unet_class_embedding(self, unet: UNet2DConditionModel): + class_embedding = unet.class_embedding + try: + unet.class_embedding = None + yield unet + finally: + unet.class_embedding = class_embedding + + @contextmanager + def set_scheduler( + self, pipe: StableDiffusionPipeline, scheduler_class: Any, **kwargs + ): + scheduler_orig = pipe.scheduler + pipe.scheduler = scheduler_class.from_config(scheduler_orig.config, **kwargs) + yield pipe + pipe.scheduler = scheduler_orig + + def get_eps_pretrain( + self, + latents_noisy: Float[Tensor, "B 4 Hl Wl"], + t: Int[Tensor, "B"], + prompt_utils: PromptProcessorOutput, + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + ) -> Float[Tensor, "B 4 Hl Wl"]: + batch_size = latents_noisy.shape[0] + + if prompt_utils.use_perp_neg: + ( + text_embeddings, + neg_guidance_weights, + ) = prompt_utils.get_text_embeddings_perp_neg( + elevation, azimuth, camera_distances, self.cfg.view_dependent_prompting + ) + with torch.no_grad(): + with self.disable_unet_class_embedding(self.pipe.unet) as unet: + noise_pred = self.forward_unet( + unet, + torch.cat([latents_noisy] * 4, dim=0), + torch.cat([t] * 4, dim=0), + encoder_hidden_states=text_embeddings, + cross_attention_kwargs={"scale": 0.0} + if self.vsd_share_model + else None, + velocity_to_epsilon=self.pipe.scheduler.config.prediction_type + == "v_prediction", + ) # (4B, 3, Hl, Wl) + + noise_pred_text = noise_pred[:batch_size] + noise_pred_uncond = noise_pred[batch_size : batch_size * 2] + noise_pred_neg = noise_pred[batch_size * 2 :] + + e_pos = noise_pred_text - noise_pred_uncond + accum_grad = 0 + n_negative_prompts = neg_guidance_weights.shape[-1] + for i in range(n_negative_prompts): + e_i_neg = noise_pred_neg[i::n_negative_prompts] - noise_pred_uncond + accum_grad += neg_guidance_weights[:, i].view( + -1, 1, 1, 1 + ) * perpendicular_component(e_i_neg, e_pos) + + noise_pred = noise_pred_uncond + self.cfg.guidance_scale * ( + e_pos + accum_grad + ) + else: + text_embeddings = prompt_utils.get_text_embeddings( + elevation, azimuth, camera_distances, self.cfg.view_dependent_prompting + ) + with torch.no_grad(): + with self.disable_unet_class_embedding(self.pipe.unet) as unet: + noise_pred = self.forward_unet( + unet, + torch.cat([latents_noisy] * 2, dim=0), + torch.cat([t] * 2, dim=0), + encoder_hidden_states=text_embeddings, + cross_attention_kwargs={"scale": 0.0} + if self.vsd_share_model + else None, + velocity_to_epsilon=self.pipe.scheduler.config.prediction_type + == "v_prediction", + ) + + noise_pred_text, noise_pred_uncond = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + self.cfg.guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + + return noise_pred + + def get_eps_phi( + self, + latents_noisy: Float[Tensor, "B 4 Hl Wl"], + t: Int[Tensor, "B"], + prompt_utils: PromptProcessorOutput, + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + camera_condition: Float[Tensor, "B ..."], + ) -> Float[Tensor, "B 4 Hl Wl"]: + batch_size = latents_noisy.shape[0] + + # not using view-dependent prompting in LoRA + text_embeddings, _ = prompt_utils.get_text_embeddings( + elevation, azimuth, camera_distances, view_dependent_prompting=False + ).chunk(2) + with torch.no_grad(): + noise_pred = self.forward_unet( + self.pipe_phi.unet, + torch.cat([latents_noisy] * 2, dim=0), + torch.cat([t] * 2, dim=0), + encoder_hidden_states=torch.cat([text_embeddings] * 2, dim=0), + class_labels=torch.cat( + [ + camera_condition.view(batch_size, -1), + torch.zeros_like(camera_condition.view(batch_size, -1)), + ], + dim=0, + ) + if self.cfg.vsd_use_camera_condition + else None, + cross_attention_kwargs={"scale": 1.0}, + velocity_to_epsilon=self.pipe_phi.scheduler.config.prediction_type + == "v_prediction", + ) + + noise_pred_camera, noise_pred_uncond = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + self.cfg.vsd_guidance_scale_phi * ( + noise_pred_camera - noise_pred_uncond + ) + + return noise_pred + + def train_phi( + self, + latents: Float[Tensor, "B 4 Hl Wl"], + prompt_utils: PromptProcessorOutput, + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + camera_condition: Float[Tensor, "B ..."], + ): + B = latents.shape[0] + latents = latents.detach().repeat( + self.cfg.vsd_lora_n_timestamp_samples, 1, 1, 1 + ) + + num_train_timesteps = self.pipe_phi.scheduler.config.num_train_timesteps + t = torch.randint( + int(num_train_timesteps * 0.0), + int(num_train_timesteps * 1.0), + [B * self.cfg.vsd_lora_n_timestamp_samples], + dtype=torch.long, + device=self.device, + ) + + noise = torch.randn_like(latents) + latents_noisy = self.pipe_phi.scheduler.add_noise(latents, noise, t) + if self.pipe_phi.scheduler.config.prediction_type == "epsilon": + target = noise + elif self.pipe_phi.scheduler.prediction_type == "v_prediction": + target = self.pipe_phi.scheduler.get_velocity(latents, noise, t) + else: + raise ValueError( + f"Unknown prediction type {self.pipe_phi.scheduler.prediction_type}" + ) + + # not using view-dependent prompting in LoRA + text_embeddings, _ = prompt_utils.get_text_embeddings( + elevation, azimuth, camera_distances, view_dependent_prompting=False + ).chunk(2) + + if ( + self.cfg.vsd_use_camera_condition + and self.cfg.vsd_lora_cfg_training + and random.random() < 0.1 + ): + camera_condition = torch.zeros_like(camera_condition) + + noise_pred = self.forward_unet( + self.pipe_phi.unet, + latents_noisy, + t, + encoder_hidden_states=text_embeddings.repeat( + self.cfg.vsd_lora_n_timestamp_samples, 1, 1 + ), + class_labels=camera_condition.view(B, -1).repeat( + self.cfg.vsd_lora_n_timestamp_samples, 1 + ) + if self.cfg.vsd_use_camera_condition + else None, + cross_attention_kwargs={"scale": 1.0}, + ) + return F.mse_loss(noise_pred.float(), target.float(), reduction="mean") + + def forward( + self, + rgb: Float[Tensor, "B H W C"], + prompt_utils: PromptProcessorOutput, + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + mvp_mtx: Float[Tensor, "B 4 4"], + c2w: Float[Tensor, "B 4 4"], + rgb_as_latents=False, + **kwargs, + ): + batch_size = rgb.shape[0] + + rgb_BCHW = rgb.permute(0, 3, 1, 2) + latents: Float[Tensor, "B 4 Hl Wl"] + rgb_BCHW_512 = F.interpolate( + rgb_BCHW, (512, 512), mode="bilinear", align_corners=False + ) + if rgb_as_latents: + # treat input rgb as latents + # input rgb should be in range [-1, 1] + latents = F.interpolate( + rgb_BCHW, (64, 64), mode="bilinear", align_corners=False + ) + else: + # treat input rgb as rgb + # input rgb should be in range [0, 1] + # encode image into latents with vae + latents = self.vae_encode(self.pipe.vae, rgb_BCHW_512 * 2.0 - 1.0) + + # sample timestep + # use the same timestep for each batch + assert self.min_step is not None and self.max_step is not None + t = torch.randint( + self.min_step, + self.max_step + 1, + [1], + dtype=torch.long, + device=self.device, + ).repeat(batch_size) + + # sample noise + noise = torch.randn_like(latents) + latents_noisy = self.scheduler.add_noise(latents, noise, t) + + eps_pretrain = self.get_eps_pretrain( + latents_noisy, t, prompt_utils, elevation, azimuth, camera_distances + ) + + latents_1step_orig = ( + 1 + / self.alphas[t].view(-1, 1, 1, 1) + * (latents_noisy - self.sigmas[t].view(-1, 1, 1, 1) * eps_pretrain) + ).detach() + + if self.cfg.guidance_type == "sds": + eps_phi = noise + elif self.cfg.guidance_type == "vsd": + if self.cfg.vsd_camera_condition_type == "extrinsics": + camera_condition = c2w + elif self.cfg.vsd_camera_condition_type == "mvp": + camera_condition = mvp_mtx + elif self.cfg.vsd_camera_condition_type == "spherical": + camera_condition = torch.stack( + [ + torch.deg2rad(elevation), + torch.sin(torch.deg2rad(azimuth)), + torch.cos(torch.deg2rad(azimuth)), + camera_distances, + ], + dim=-1, + ) + else: + raise ValueError( + f"Unknown camera_condition_type {self.cfg.vsd_camera_condition_type}" + ) + eps_phi = self.get_eps_phi( + latents_noisy, + t, + prompt_utils, + elevation, + azimuth, + camera_distances, + camera_condition, + ) + + loss_train_phi = self.train_phi( + latents, + prompt_utils, + elevation, + azimuth, + camera_distances, + camera_condition, + ) + + if self.cfg.weighting_strategy == "dreamfusion": + w = (1.0 - self.alphas[t]).view(-1, 1, 1, 1) + elif self.cfg.weighting_strategy == "uniform": + w = 1.0 + elif self.cfg.weighting_strategy == "fantasia3d": + w = (self.alphas[t] ** 0.5 * (1 - self.alphas[t])).view(-1, 1, 1, 1) + else: + raise ValueError( + f"Unknown weighting strategy: {self.cfg.weighting_strategy}" + ) + + grad = w * (eps_pretrain - eps_phi) + + # compute decoded image if needed for visualization/img loss + if self.cfg.return_rgb_1step_orig or self.cfg.use_img_loss: + with torch.no_grad(): + image_denoised_pretrain = self.vae_decode( + self.pipe.vae, latents_1step_orig + ) + rgb_1step_orig = image_denoised_pretrain.permute(0, 2, 3, 1) + + if self.grad_clip_val is not None: + grad = grad.clamp(-self.grad_clip_val, self.grad_clip_val) + + # reparameterization trick: + # d(loss)/d(latents) = latents - target = latents - (latents - grad) = grad + target = (latents - grad).detach() + loss_sd = 0.5 * F.mse_loss(latents, target, reduction="sum") / batch_size + + guidance_out = { + "loss_sd": loss_sd, + "grad_norm": grad.norm(), + "timesteps": t, + "min_step": self.min_step, + "max_step": self.max_step, + "latents": latents, + "latents_1step_orig": latents_1step_orig, + "rgb": rgb_BCHW.permute(0, 2, 3, 1), + "weights": w, + "lambdas": self.lambdas[t], + } + + # image-space loss proposed in HiFA: https://hifa-team.github.io/HiFA-site + if self.cfg.use_img_loss: + if self.cfg.guidance_type == "vsd": + latents_denoised_est = ( + latents_noisy - self.sigmas[t] * eps_phi + ) / self.alphas[t].view(-1, 1, 1, 1) + image_denoised_est = self.vae_decode( + self.pipe.vae, latents_denoised_est + ) + else: + image_denoised_est = rgb_BCHW_512 + grad_img = ( + w + * (image_denoised_est - image_denoised_pretrain) + * self.alphas[t].view(-1, 1, 1, 1) + / self.sigmas[t].view(-1, 1, 1, 1) + ) + if self.grad_clip_val is not None: + grad_img = grad_img.clamp(-self.grad_clip_val, self.grad_clip_val) + target_img = (rgb_BCHW_512 - grad_img).detach() + loss_sd_img = ( + 0.5 * F.mse_loss(rgb_BCHW_512, target_img, reduction="sum") / batch_size + ) + guidance_out.update({"loss_sd_img": loss_sd_img}) + + if self.cfg.return_rgb_1step_orig: + guidance_out.update({"rgb_1step_orig": rgb_1step_orig}) + + if self.cfg.return_rgb_multistep_orig: + with self.set_scheduler( + self.pipe, + DPMSolverSinglestepScheduler, + solver_order=1, + num_train_timesteps=int(t[0]), + ) as pipe: + text_embeddings = prompt_utils.get_text_embeddings( + elevation, + azimuth, + camera_distances, + self.cfg.view_dependent_prompting, + ) + text_embeddings_cond, text_embeddings_uncond = text_embeddings.chunk(2) + with torch.cuda.amp.autocast(enabled=False): + latents_multistep_orig = pipe( + num_inference_steps=self.cfg.n_rgb_multistep_orig_steps, + guidance_scale=self.cfg.guidance_scale, + eta=1.0, + latents=latents_noisy.to(pipe.unet.dtype), + prompt_embeds=text_embeddings_cond.to(pipe.unet.dtype), + negative_prompt_embeds=text_embeddings_uncond.to( + pipe.unet.dtype + ), + cross_attention_kwargs={"scale": 0.0} + if self.vsd_share_model + else None, + output_type="latent", + ).images.to(latents.dtype) + with torch.no_grad(): + rgb_multistep_orig = self.vae_decode( + self.pipe.vae, latents_multistep_orig + ) + guidance_out.update( + { + "latents_multistep_orig": latents_multistep_orig, + "rgb_multistep_orig": rgb_multistep_orig.permute(0, 2, 3, 1), + } + ) + + if self.cfg.guidance_type == "vsd": + guidance_out.update( + { + "loss_train_phi": loss_train_phi, + } + ) + + return guidance_out + + @torch.cuda.amp.autocast(enabled=False) + def set_min_max_steps(self, min_step_percent=0.02, max_step_percent=0.98): + self.min_step = int(self.num_train_timesteps * min_step_percent) + self.max_step = int(self.num_train_timesteps * max_step_percent) + + def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False): + # clip grad for stable training as demonstrated in + # Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation + # http://arxiv.org/abs/2303.15413 + if self.cfg.grad_clip is not None: + self.grad_clip_val = C(self.cfg.grad_clip, epoch, global_step) + + if self.cfg.sqrt_anneal: + percentage = ( + float(global_step) / self.cfg.trainer_max_steps + ) ** 0.5 # progress percentage + if type(self.cfg.max_step_percent) not in [float, int]: + max_step_percent = self.cfg.max_step_percent[1] + else: + max_step_percent = self.cfg.max_step_percent + curr_percent = ( + max_step_percent - C(self.cfg.min_step_percent, epoch, global_step) + ) * (1 - percentage) + C(self.cfg.min_step_percent, epoch, global_step) + self.set_min_max_steps( + min_step_percent=curr_percent, + max_step_percent=curr_percent, + ) + else: + self.set_min_max_steps( + min_step_percent=C(self.cfg.min_step_percent, epoch, global_step), + max_step_percent=C(self.cfg.max_step_percent, epoch, global_step), + ) diff --git a/threestudio/models/guidance/stable_diffusion_vsd_guidance.py b/threestudio/models/guidance/stable_diffusion_vsd_guidance.py new file mode 100644 index 0000000..8a380ba --- /dev/null +++ b/threestudio/models/guidance/stable_diffusion_vsd_guidance.py @@ -0,0 +1,723 @@ +import random +from contextlib import contextmanager +from dataclasses import dataclass, field + +import torch +import torch.nn as nn +import torch.nn.functional as F +from diffusers import ( + DDPMScheduler, + DPMSolverMultistepScheduler, + StableDiffusionPipeline, + UNet2DConditionModel, +) +from diffusers.loaders import AttnProcsLayers +from diffusers.models.attention_processor import LoRAAttnProcessor +from diffusers.models.embeddings import TimestepEmbedding +from diffusers.utils.import_utils import is_xformers_available + +import threestudio +from threestudio.models.prompt_processors.base import PromptProcessorOutput +from threestudio.utils.base import BaseModule +from threestudio.utils.misc import C, cleanup, parse_version +from threestudio.utils.typing import * + + +class ToWeightsDType(nn.Module): + def __init__(self, module: nn.Module, dtype: torch.dtype): + super().__init__() + self.module = module + self.dtype = dtype + + def forward(self, x: Float[Tensor, "..."]) -> Float[Tensor, "..."]: + return self.module(x).to(self.dtype) + + +@threestudio.register("stable-diffusion-vsd-guidance") +class StableDiffusionVSDGuidance(BaseModule): + @dataclass + class Config(BaseModule.Config): + pretrained_model_name_or_path: str = "stabilityai/stable-diffusion-2-1-base" + pretrained_model_name_or_path_lora: str = "stabilityai/stable-diffusion-2-1" + enable_memory_efficient_attention: bool = False + enable_sequential_cpu_offload: bool = False + enable_attention_slicing: bool = False + enable_channels_last_format: bool = False + guidance_scale: float = 7.5 + guidance_scale_lora: float = 1.0 + grad_clip: Optional[ + Any + ] = None # field(default_factory=lambda: [0, 2.0, 8.0, 1000]) + half_precision_weights: bool = True + lora_cfg_training: bool = True + lora_n_timestamp_samples: int = 1 + + min_step_percent: float = 0.02 + max_step_percent: float = 0.98 + sqrt_anneal: bool = False # sqrt anneal proposed in HiFA: https://hifa-team.github.io/HiFA-site/ + trainer_max_steps: int = 25000 + use_img_loss: bool = False # image-space SDS proposed in HiFA: https://hifa-team.github.io/HiFA-site/ + + view_dependent_prompting: bool = True + camera_condition_type: str = "extrinsics" + + cfg: Config + + def configure(self) -> None: + threestudio.info(f"Loading Stable Diffusion ...") + + self.weights_dtype = ( + torch.float16 if self.cfg.half_precision_weights else torch.float32 + ) + + pipe_kwargs = { + "tokenizer": None, + "safety_checker": None, + "feature_extractor": None, + "requires_safety_checker": False, + "torch_dtype": self.weights_dtype, + } + + pipe_lora_kwargs = { + "tokenizer": None, + "safety_checker": None, + "feature_extractor": None, + "requires_safety_checker": False, + "torch_dtype": self.weights_dtype, + } + + @dataclass + class SubModules: + pipe: StableDiffusionPipeline + pipe_lora: StableDiffusionPipeline + + pipe = StableDiffusionPipeline.from_pretrained( + self.cfg.pretrained_model_name_or_path, + **pipe_kwargs, + ).to(self.device) + if ( + self.cfg.pretrained_model_name_or_path + == self.cfg.pretrained_model_name_or_path_lora + ): + self.single_model = True + pipe_lora = pipe + else: + self.single_model = False + pipe_lora = StableDiffusionPipeline.from_pretrained( + self.cfg.pretrained_model_name_or_path_lora, + **pipe_lora_kwargs, + ).to(self.device) + del pipe_lora.vae + cleanup() + pipe_lora.vae = pipe.vae + self.submodules = SubModules(pipe=pipe, pipe_lora=pipe_lora) + + if self.cfg.enable_memory_efficient_attention: + if parse_version(torch.__version__) >= parse_version("2"): + threestudio.info( + "PyTorch2.0 uses memory efficient attention by default." + ) + elif not is_xformers_available(): + threestudio.warn( + "xformers is not available, memory efficient attention is not enabled." + ) + else: + self.pipe.enable_xformers_memory_efficient_attention() + self.pipe_lora.enable_xformers_memory_efficient_attention() + + if self.cfg.enable_sequential_cpu_offload: + self.pipe.enable_sequential_cpu_offload() + self.pipe_lora.enable_sequential_cpu_offload() + + if self.cfg.enable_attention_slicing: + self.pipe.enable_attention_slicing(1) + self.pipe_lora.enable_attention_slicing(1) + + if self.cfg.enable_channels_last_format: + self.pipe.unet.to(memory_format=torch.channels_last) + self.pipe_lora.unet.to(memory_format=torch.channels_last) + + del self.pipe.text_encoder + if not self.single_model: + del self.pipe_lora.text_encoder + cleanup() + + for p in self.vae.parameters(): + p.requires_grad_(False) + for p in self.unet.parameters(): + p.requires_grad_(False) + for p in self.unet_lora.parameters(): + p.requires_grad_(False) + + # FIXME: hard-coded dims + self.camera_embedding = ToWeightsDType( + TimestepEmbedding(16, 1280), self.weights_dtype + ).to(self.device) + self.unet_lora.class_embedding = self.camera_embedding + + # set up LoRA layers + lora_attn_procs = {} + for name in self.unet_lora.attn_processors.keys(): + cross_attention_dim = ( + None + if name.endswith("attn1.processor") + else self.unet_lora.config.cross_attention_dim + ) + if name.startswith("mid_block"): + hidden_size = self.unet_lora.config.block_out_channels[-1] + elif name.startswith("up_blocks"): + block_id = int(name[len("up_blocks.")]) + hidden_size = list(reversed(self.unet_lora.config.block_out_channels))[ + block_id + ] + elif name.startswith("down_blocks"): + block_id = int(name[len("down_blocks.")]) + hidden_size = self.unet_lora.config.block_out_channels[block_id] + + lora_attn_procs[name] = LoRAAttnProcessor( + hidden_size=hidden_size, cross_attention_dim=cross_attention_dim + ) + + self.unet_lora.set_attn_processor(lora_attn_procs) + + self.lora_layers = AttnProcsLayers(self.unet_lora.attn_processors).to( + self.device + ) + self.lora_layers._load_state_dict_pre_hooks.clear() + self.lora_layers._state_dict_hooks.clear() + + self.scheduler = DDPMScheduler.from_pretrained( + self.cfg.pretrained_model_name_or_path, + subfolder="scheduler", + torch_dtype=self.weights_dtype, + ) + + self.scheduler_lora = DDPMScheduler.from_pretrained( + self.cfg.pretrained_model_name_or_path_lora, + subfolder="scheduler", + torch_dtype=self.weights_dtype, + ) + + self.scheduler_sample = DPMSolverMultistepScheduler.from_config( + self.pipe.scheduler.config + ) + self.scheduler_lora_sample = DPMSolverMultistepScheduler.from_config( + self.pipe_lora.scheduler.config + ) + + self.pipe.scheduler = self.scheduler + self.pipe_lora.scheduler = self.scheduler_lora + + self.num_train_timesteps = self.scheduler.config.num_train_timesteps + self.set_min_max_steps() # set to default value + + self.alphas: Float[Tensor, "..."] = self.scheduler.alphas_cumprod.to( + self.device + ) + + self.grad_clip_val: Optional[float] = None + + threestudio.info(f"Loaded Stable Diffusion!") + + @torch.cuda.amp.autocast(enabled=False) + def set_min_max_steps(self, min_step_percent=0.02, max_step_percent=0.98): + self.min_step = int(self.num_train_timesteps * min_step_percent) + self.max_step = int(self.num_train_timesteps * max_step_percent) + + @property + def pipe(self): + return self.submodules.pipe + + @property + def pipe_lora(self): + return self.submodules.pipe_lora + + @property + def unet(self): + return self.submodules.pipe.unet + + @property + def unet_lora(self): + return self.submodules.pipe_lora.unet + + @property + def vae(self): + return self.submodules.pipe.vae + + @property + def vae_lora(self): + return self.submodules.pipe_lora.vae + + @torch.no_grad() + @torch.cuda.amp.autocast(enabled=False) + def _sample( + self, + pipe: StableDiffusionPipeline, + sample_scheduler: DPMSolverMultistepScheduler, + text_embeddings: Float[Tensor, "BB N Nf"], + num_inference_steps: int, + guidance_scale: float, + num_images_per_prompt: int = 1, + height: Optional[int] = None, + width: Optional[int] = None, + class_labels: Optional[Float[Tensor, "BB 16"]] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + ) -> Float[Tensor, "B H W 3"]: + vae_scale_factor = 2 ** (len(pipe.vae.config.block_out_channels) - 1) + height = height or pipe.unet.config.sample_size * vae_scale_factor + width = width or pipe.unet.config.sample_size * vae_scale_factor + batch_size = text_embeddings.shape[0] // 2 + device = self.device + + sample_scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = sample_scheduler.timesteps + num_channels_latents = pipe.unet.config.in_channels + + latents = pipe.prepare_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + height, + width, + self.weights_dtype, + device, + generator, + ) + + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) + latent_model_input = sample_scheduler.scale_model_input( + latent_model_input, t + ) + + # predict the noise residual + if class_labels is None: + with self.disable_unet_class_embedding(pipe.unet) as unet: + noise_pred = unet( + latent_model_input, + t, + encoder_hidden_states=text_embeddings.to(self.weights_dtype), + cross_attention_kwargs=cross_attention_kwargs, + ).sample + else: + noise_pred = pipe.unet( + latent_model_input, + t, + encoder_hidden_states=text_embeddings.to(self.weights_dtype), + class_labels=class_labels, + cross_attention_kwargs=cross_attention_kwargs, + ).sample + + noise_pred_text, noise_pred_uncond = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + + # compute the previous noisy sample x_t -> x_t-1 + latents = sample_scheduler.step(noise_pred, t, latents).prev_sample + + latents = 1 / pipe.vae.config.scaling_factor * latents + images = pipe.vae.decode(latents).sample + images = (images / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 + images = images.permute(0, 2, 3, 1).float() + return images + + def sample( + self, + prompt_utils: PromptProcessorOutput, + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + seed: int = 0, + **kwargs, + ) -> Float[Tensor, "N H W 3"]: + # view-dependent text embeddings + text_embeddings_vd = prompt_utils.get_text_embeddings( + elevation, + azimuth, + camera_distances, + view_dependent_prompting=self.cfg.view_dependent_prompting, + ) + cross_attention_kwargs = {"scale": 0.0} if self.single_model else None + generator = torch.Generator(device=self.device).manual_seed(seed) + + return self._sample( + pipe=self.pipe, + sample_scheduler=self.scheduler_sample, + text_embeddings=text_embeddings_vd, + num_inference_steps=25, + guidance_scale=self.cfg.guidance_scale, + cross_attention_kwargs=cross_attention_kwargs, + generator=generator, + ) + + def sample_lora( + self, + prompt_utils: PromptProcessorOutput, + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + mvp_mtx: Float[Tensor, "B 4 4"], + c2w: Float[Tensor, "B 4 4"], + seed: int = 0, + **kwargs, + ) -> Float[Tensor, "N H W 3"]: + # input text embeddings, view-independent + text_embeddings = prompt_utils.get_text_embeddings( + elevation, azimuth, camera_distances, view_dependent_prompting=False + ) + + if self.cfg.camera_condition_type == "extrinsics": + camera_condition = c2w + elif self.cfg.camera_condition_type == "mvp": + camera_condition = mvp_mtx + else: + raise ValueError( + f"Unknown camera_condition_type {self.cfg.camera_condition_type}" + ) + + B = elevation.shape[0] + camera_condition_cfg = torch.cat( + [ + camera_condition.view(B, -1), + torch.zeros_like(camera_condition.view(B, -1)), + ], + dim=0, + ) + + generator = torch.Generator(device=self.device).manual_seed(seed) + return self._sample( + sample_scheduler=self.scheduler_lora_sample, + pipe=self.pipe_lora, + text_embeddings=text_embeddings, + num_inference_steps=25, + guidance_scale=self.cfg.guidance_scale_lora, + class_labels=camera_condition_cfg, + cross_attention_kwargs={"scale": 1.0}, + generator=generator, + ) + + @torch.cuda.amp.autocast(enabled=False) + def forward_unet( + self, + unet: UNet2DConditionModel, + latents: Float[Tensor, "..."], + t: Float[Tensor, "..."], + encoder_hidden_states: Float[Tensor, "..."], + class_labels: Optional[Float[Tensor, "B 16"]] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + ) -> Float[Tensor, "..."]: + input_dtype = latents.dtype + return unet( + latents.to(self.weights_dtype), + t.to(self.weights_dtype), + encoder_hidden_states=encoder_hidden_states.to(self.weights_dtype), + class_labels=class_labels, + cross_attention_kwargs=cross_attention_kwargs, + ).sample.to(input_dtype) + + @torch.cuda.amp.autocast(enabled=False) + def encode_images( + self, imgs: Float[Tensor, "B 3 512 512"] + ) -> Float[Tensor, "B 4 64 64"]: + input_dtype = imgs.dtype + imgs = imgs * 2.0 - 1.0 + posterior = self.vae.encode(imgs.to(self.weights_dtype)).latent_dist + latents = posterior.sample() * self.vae.config.scaling_factor + return latents.to(input_dtype) + + @torch.cuda.amp.autocast(enabled=False) + def decode_latents( + self, + latents: Float[Tensor, "B 4 H W"], + latent_height: int = 64, + latent_width: int = 64, + ) -> Float[Tensor, "B 3 512 512"]: + input_dtype = latents.dtype + latents = F.interpolate( + latents, (latent_height, latent_width), mode="bilinear", align_corners=False + ) + latents = 1 / self.vae.config.scaling_factor * latents + image = self.vae.decode(latents.to(self.weights_dtype)).sample + image = (image * 0.5 + 0.5).clamp(0, 1) + return image.to(input_dtype) + + @contextmanager + def disable_unet_class_embedding(self, unet: UNet2DConditionModel): + class_embedding = unet.class_embedding + try: + unet.class_embedding = None + yield unet + finally: + unet.class_embedding = class_embedding + + def compute_grad_vsd( + self, + latents: Float[Tensor, "B 4 64 64"], + text_embeddings_vd: Float[Tensor, "BB 77 768"], + text_embeddings: Float[Tensor, "BB 77 768"], + camera_condition: Float[Tensor, "B 4 4"], + ): + B = latents.shape[0] + + with torch.no_grad(): + # random timestamp + t = torch.randint( + self.min_step, + self.max_step + 1, + [B], + dtype=torch.long, + device=self.device, + ) + + # add noise + noise = torch.randn_like(latents) + latents_noisy = self.scheduler.add_noise(latents, noise, t) + # pred noise + latent_model_input = torch.cat([latents_noisy] * 2, dim=0) + with self.disable_unet_class_embedding(self.unet) as unet: + cross_attention_kwargs = {"scale": 0.0} if self.single_model else None + noise_pred_pretrain = self.forward_unet( + unet, + latent_model_input, + torch.cat([t] * 2), + encoder_hidden_states=text_embeddings_vd, + cross_attention_kwargs=cross_attention_kwargs, + ) + + # use view-independent text embeddings in LoRA + text_embeddings_cond, _ = text_embeddings.chunk(2) + noise_pred_est = self.forward_unet( + self.unet_lora, + latent_model_input, + torch.cat([t] * 2), + encoder_hidden_states=torch.cat([text_embeddings_cond] * 2), + class_labels=torch.cat( + [ + camera_condition.view(B, -1), + torch.zeros_like(camera_condition.view(B, -1)), + ], + dim=0, + ), + cross_attention_kwargs={"scale": 1.0}, + ) + + ( + noise_pred_pretrain_text, + noise_pred_pretrain_uncond, + ) = noise_pred_pretrain.chunk(2) + + # NOTE: guidance scale definition here is aligned with diffusers, but different from other guidance + noise_pred_pretrain = noise_pred_pretrain_uncond + self.cfg.guidance_scale * ( + noise_pred_pretrain_text - noise_pred_pretrain_uncond + ) + + # TODO: more general cases + assert self.scheduler.config.prediction_type == "epsilon" + if self.scheduler_lora.config.prediction_type == "v_prediction": + alphas_cumprod = self.scheduler_lora.alphas_cumprod.to( + device=latents_noisy.device, dtype=latents_noisy.dtype + ) + alpha_t = alphas_cumprod[t] ** 0.5 + sigma_t = (1 - alphas_cumprod[t]) ** 0.5 + + noise_pred_est = latent_model_input * torch.cat([sigma_t] * 2, dim=0).view( + -1, 1, 1, 1 + ) + noise_pred_est * torch.cat([alpha_t] * 2, dim=0).view(-1, 1, 1, 1) + + ( + noise_pred_est_camera, + noise_pred_est_uncond, + ) = noise_pred_est.chunk(2) + + # NOTE: guidance scale definition here is aligned with diffusers, but different from other guidance + noise_pred_est = noise_pred_est_uncond + self.cfg.guidance_scale_lora * ( + noise_pred_est_camera - noise_pred_est_uncond + ) + + w = (1 - self.alphas[t]).view(-1, 1, 1, 1) + + grad = w * (noise_pred_pretrain - noise_pred_est) + + alpha = (self.alphas[t] ** 0.5).view(-1, 1, 1, 1) + sigma = ((1 - self.alphas[t]) ** 0.5).view(-1, 1, 1, 1) + # image-space SDS proposed in HiFA: https://hifa-team.github.io/HiFA-site/ + if self.cfg.use_img_loss: + latents_denoised_pretrain = ( + latents_noisy - sigma * noise_pred_pretrain + ) / alpha + latents_denoised_est = (latents_noisy - sigma * noise_pred_est) / alpha + image_denoised_pretrain = self.decode_latents(latents_denoised_pretrain) + image_denoised_est = self.decode_latents(latents_denoised_est) + grad_img = ( + w * (image_denoised_est - image_denoised_pretrain) * alpha / sigma + ) + else: + grad_img = None + return grad, grad_img + + def train_lora( + self, + latents: Float[Tensor, "B 4 64 64"], + text_embeddings: Float[Tensor, "BB 77 768"], + camera_condition: Float[Tensor, "B 4 4"], + ): + B = latents.shape[0] + latents = latents.detach().repeat(self.cfg.lora_n_timestamp_samples, 1, 1, 1) + + t = torch.randint( + int(self.num_train_timesteps * 0.0), + int(self.num_train_timesteps * 1.0), + [B * self.cfg.lora_n_timestamp_samples], + dtype=torch.long, + device=self.device, + ) + + noise = torch.randn_like(latents) + noisy_latents = self.scheduler_lora.add_noise(latents, noise, t) + if self.scheduler_lora.config.prediction_type == "epsilon": + target = noise + elif self.scheduler_lora.config.prediction_type == "v_prediction": + target = self.scheduler_lora.get_velocity(latents, noise, t) + else: + raise ValueError( + f"Unknown prediction type {self.scheduler_lora.config.prediction_type}" + ) + # use view-independent text embeddings in LoRA + text_embeddings_cond, _ = text_embeddings.chunk(2) + if self.cfg.lora_cfg_training and random.random() < 0.1: + camera_condition = torch.zeros_like(camera_condition) + noise_pred = self.forward_unet( + self.unet_lora, + noisy_latents, + t, + encoder_hidden_states=text_embeddings_cond.repeat( + self.cfg.lora_n_timestamp_samples, 1, 1 + ), + class_labels=camera_condition.view(B, -1).repeat( + self.cfg.lora_n_timestamp_samples, 1 + ), + cross_attention_kwargs={"scale": 1.0}, + ) + return F.mse_loss(noise_pred.float(), target.float(), reduction="mean") + + def get_latents( + self, rgb_BCHW: Float[Tensor, "B C H W"], rgb_as_latents=False + ) -> Float[Tensor, "B 4 64 64"]: + rgb_BCHW_512 = F.interpolate( + rgb_BCHW, (512, 512), mode="bilinear", align_corners=False + ) + if rgb_as_latents: + latents = F.interpolate( + rgb_BCHW, (64, 64), mode="bilinear", align_corners=False + ) + else: + # encode image into latents with vae + latents = self.encode_images(rgb_BCHW_512) + return latents, rgb_BCHW_512 + + def forward( + self, + rgb: Float[Tensor, "B H W C"], + prompt_utils: PromptProcessorOutput, + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + mvp_mtx: Float[Tensor, "B 4 4"], + c2w: Float[Tensor, "B 4 4"], + rgb_as_latents=False, + **kwargs, + ): + batch_size = rgb.shape[0] + + rgb_BCHW = rgb.permute(0, 3, 1, 2) + latents, rgb_BCHW_512 = self.get_latents( + rgb_BCHW, rgb_as_latents=rgb_as_latents + ) + + # view-dependent text embeddings + text_embeddings_vd = prompt_utils.get_text_embeddings( + elevation, + azimuth, + camera_distances, + view_dependent_prompting=self.cfg.view_dependent_prompting, + ) + + # input text embeddings, view-independent + text_embeddings = prompt_utils.get_text_embeddings( + elevation, azimuth, camera_distances, view_dependent_prompting=False + ) + + if self.cfg.camera_condition_type == "extrinsics": + camera_condition = c2w + elif self.cfg.camera_condition_type == "mvp": + camera_condition = mvp_mtx + else: + raise ValueError( + f"Unknown camera_condition_type {self.cfg.camera_condition_type}" + ) + + grad, grad_img = self.compute_grad_vsd( + latents, text_embeddings_vd, text_embeddings, camera_condition + ) + + grad = torch.nan_to_num(grad) + # clip grad for stable training? + if self.grad_clip_val is not None: + grad = grad.clamp(-self.grad_clip_val, self.grad_clip_val) + + # reparameterization trick + # d(loss)/d(latents) = latents - target = latents - (latents - grad) = grad + target = (latents - grad).detach() + loss_vsd = 0.5 * F.mse_loss(latents, target, reduction="sum") / batch_size + loss_lora = self.train_lora(latents, text_embeddings, camera_condition) + + loss_dict = { + "loss_vsd": loss_vsd, + "loss_lora": loss_lora, + "grad_norm": grad.norm(), + "min_step": self.min_step, + "max_step": self.max_step, + } + + if self.cfg.use_img_loss: + grad_img = torch.nan_to_num(grad_img) + if self.grad_clip_val is not None: + grad_img = grad_img.clamp(-self.grad_clip_val, self.grad_clip_val) + target_img = (rgb_BCHW_512 - grad_img).detach() + loss_vsd_img = ( + 0.5 * F.mse_loss(rgb_BCHW_512, target_img, reduction="sum") / batch_size + ) + loss_dict["loss_vsd_img"] = loss_vsd_img + + return loss_dict + + def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False): + # clip grad for stable training as demonstrated in + # Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation + # http://arxiv.org/abs/2303.15413 + if self.cfg.grad_clip is not None: + self.grad_clip_val = C(self.cfg.grad_clip, epoch, global_step) + + if self.cfg.sqrt_anneal: + percentage = ( + float(global_step) / self.cfg.trainer_max_steps + ) ** 0.5 # progress percentage + if type(self.cfg.max_step_percent) not in [float, int]: + max_step_percent = self.cfg.max_step_percent[1] + else: + max_step_percent = self.cfg.max_step_percent + curr_percent = ( + max_step_percent - C(self.cfg.min_step_percent, epoch, global_step) + ) * (1 - percentage) + C(self.cfg.min_step_percent, epoch, global_step) + self.set_min_max_steps( + min_step_percent=curr_percent, + max_step_percent=curr_percent, + ) + else: + self.set_min_max_steps( + min_step_percent=C(self.cfg.min_step_percent, epoch, global_step), + max_step_percent=C(self.cfg.max_step_percent, epoch, global_step), + ) diff --git a/threestudio/models/guidance/stable_zero123_guidance.py b/threestudio/models/guidance/stable_zero123_guidance.py new file mode 100644 index 0000000..bc452be --- /dev/null +++ b/threestudio/models/guidance/stable_zero123_guidance.py @@ -0,0 +1,362 @@ +import importlib +import os +from dataclasses import dataclass, field + +import cv2 +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from diffusers import DDIMScheduler, DDPMScheduler, StableDiffusionPipeline +from diffusers.utils.import_utils import is_xformers_available +from omegaconf import OmegaConf +from tqdm import tqdm + +import threestudio +from threestudio.utils.base import BaseObject +from threestudio.utils.misc import C, parse_version +from threestudio.utils.typing import * + +import clip + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +def instantiate_from_config(config): + if not "target" in config: + if config == "__is_first_stage__": + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +# load model +def load_model_from_config(config, ckpt, device, vram_O=True, verbose=False): + pl_sd = torch.load(ckpt, map_location="cpu") + + if "global_step" in pl_sd and verbose: + print(f'[INFO] Global Step: {pl_sd["global_step"]}') + + sd = pl_sd["state_dict"] + + model = instantiate_from_config(config.model) + m, u = model.load_state_dict(sd, strict=False) + + if len(m) > 0 and verbose: + print("[INFO] missing keys: \n", m) + if len(u) > 0 and verbose: + print("[INFO] unexpected keys: \n", u) + + # manually load ema and delete it to save GPU memory + if model.use_ema: + if verbose: + print("[INFO] loading EMA...") + model.model_ema.copy_to(model.model) + del model.model_ema + + if vram_O: + # we don't need decoder + del model.first_stage_model.decoder + + torch.cuda.empty_cache() + + model.eval().to(device) + + return model + + +@threestudio.register("stable-zero123-guidance") +class StableZero123Guidance(BaseObject): + @dataclass + class Config(BaseObject.Config): + pretrained_model_name_or_path: str = "load/zero123/stable-zero123.ckpt" + pretrained_config: str = "load/zero123/sd-objaverse-finetune-c_concat-256.yaml" + vram_O: bool = True + + cond_image_path: str = "load/images/hamburger_rgba.png" + cond_elevation_deg: float = 0.0 + cond_azimuth_deg: float = 0.0 + cond_camera_distance: float = 1.2 + + guidance_scale: float = 5.0 + + grad_clip: Optional[ + Any + ] = None # field(default_factory=lambda: [0, 2.0, 8.0, 1000]) + half_precision_weights: bool = True + + min_step_percent: float = 0.02 + max_step_percent: float = 0.98 + + cfg: Config + + def configure(self) -> None: + threestudio.info(f"Loading Stable Zero123 ...") + + self.config = OmegaConf.load(self.cfg.pretrained_config) + # TODO: seems it cannot load into fp16... + # self.weights_dtype = torch.float32 + self.weights_dtype = ( + torch.float16 if self.cfg.half_precision_weights else torch.float32 + ) + self.model = load_model_from_config( + self.config, + self.cfg.pretrained_model_name_or_path, + device=self.device, + vram_O=self.cfg.vram_O, + ) + self.model.to(dtype=self.weights_dtype) + # self.model.cond_stage_model.model.visual.ln_pre.to(dtype=torch.float32) + def recursive_layernorm_fp32(model): + for attr in model.__dir__(): + if attr.startswith("_"): + continue + try: + module = getattr(model, attr) + except: + continue # ignore attributes like property, which can't be retrived using getattr? + if isinstance(module, clip.model.LayerNorm): + module.to(dtype=torch.float32) + elif isinstance(module, torch.nn.Sequential): + for sub_module in module: + recursive_layernorm_fp32(sub_module) + elif isinstance(module, torch.nn.Module): + recursive_layernorm_fp32(module) + recursive_layernorm_fp32(self.model) + + for p in self.model.parameters(): + p.requires_grad_(False) + + # timesteps: use diffuser for convenience... hope it's alright. + self.num_train_timesteps = self.config.model.params.timesteps + + self.scheduler = DDIMScheduler( + self.num_train_timesteps, + self.config.model.params.linear_start, + self.config.model.params.linear_end, + beta_schedule="scaled_linear", + clip_sample=False, + set_alpha_to_one=False, + steps_offset=1, + ) + + self.num_train_timesteps = self.scheduler.config.num_train_timesteps + self.set_min_max_steps() # set to default value + + self.alphas: Float[Tensor, "..."] = self.scheduler.alphas_cumprod.to( + self.device + ) + + self.grad_clip_val: Optional[float] = None + + self.prepare_embeddings(self.cfg.cond_image_path) + + threestudio.info(f"Loaded Stable Zero123!") + + @torch.cuda.amp.autocast(enabled=False) + def set_min_max_steps(self, min_step_percent=0.02, max_step_percent=0.98): + self.min_step = int(self.num_train_timesteps * min_step_percent) + self.max_step = int(self.num_train_timesteps * max_step_percent) + + @torch.cuda.amp.autocast(enabled=False) + def prepare_embeddings(self, image_path: str) -> None: + # load cond image for zero123 + assert os.path.exists(image_path) + rgba = cv2.cvtColor( + cv2.imread(image_path, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGRA2RGBA + ) + rgba = ( + cv2.resize(rgba, (256, 256), interpolation=cv2.INTER_AREA).astype( + np.float32 + ) + / 255.0 + ) + rgb = rgba[..., :3] * rgba[..., 3:] + (1 - rgba[..., 3:]) + self.rgb_256: Float[Tensor, "1 3 H W"] = ( + torch.from_numpy(rgb) + .unsqueeze(0) + .permute(0, 3, 1, 2) + .contiguous() + .to(self.device) + ) + self.c_crossattn, self.c_concat = self.get_img_embeds(self.rgb_256) + + @torch.cuda.amp.autocast(enabled=False) + @torch.no_grad() + def get_img_embeds( + self, + img: Float[Tensor, "B 3 256 256"], + ) -> Tuple[Float[Tensor, "B 1 768"], Float[Tensor, "B 4 32 32"]]: + img = img * 2.0 - 1.0 + c_crossattn = self.model.get_learned_conditioning(img.to(self.weights_dtype)).to(self.weights_dtype) + c_concat = self.model.encode_first_stage(img.to(self.weights_dtype)).mode() + return c_crossattn, c_concat + + @torch.cuda.amp.autocast(enabled=False) + def encode_images( + self, imgs: Float[Tensor, "B 3 256 256"] + ) -> Float[Tensor, "B 4 32 32"]: + input_dtype = imgs.dtype + imgs = imgs * 2.0 - 1.0 + latents = self.model.get_first_stage_encoding( + self.model.encode_first_stage(imgs.to(self.weights_dtype)) + ) + return latents.to(input_dtype) # [B, 4, 32, 32] Latent space image + + @torch.cuda.amp.autocast(enabled=False) + def decode_latents( + self, + latents: Float[Tensor, "B 4 H W"], + ) -> Float[Tensor, "B 3 512 512"]: + input_dtype = latents.dtype + image = self.model.decode_first_stage(latents) + image = (image * 0.5 + 0.5).clamp(0, 1) + return image.to(input_dtype) + + @torch.cuda.amp.autocast(enabled=False) + @torch.no_grad() + def get_cond( + self, + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + c_crossattn=None, + c_concat=None, + **kwargs, + ) -> dict: + T = torch.stack( + [ + torch.deg2rad( + (90 - elevation) - (90 - self.cfg.cond_elevation_deg) + ), # Zero123 polar is 90-elevation + torch.sin(torch.deg2rad(azimuth - self.cfg.cond_azimuth_deg)), + torch.cos(torch.deg2rad(azimuth - self.cfg.cond_azimuth_deg)), + torch.deg2rad( + 90 - torch.full_like(elevation, self.cfg.cond_elevation_deg) + ), + ], + dim=-1, + )[:, None, :].to(self.device, dtype=self.weights_dtype) + cond = {} + clip_emb = self.model.cc_projection( + torch.cat( + [ + (self.c_crossattn if c_crossattn is None else c_crossattn).repeat( + len(T), 1, 1 + ), + T, + ], + dim=-1, + ) + ) + cond["c_crossattn"] = [ + torch.cat([torch.zeros_like(clip_emb).to(self.device), clip_emb], dim=0) + ] + cond["c_concat"] = [ + torch.cat( + [ + torch.zeros_like(self.c_concat) + .repeat(len(T), 1, 1, 1) + .to(self.device), + (self.c_concat if c_concat is None else c_concat).repeat( + len(T), 1, 1, 1 + ), + ], + dim=0, + ) + ] + return cond + + def __call__( + self, + rgb: Float[Tensor, "B H W C"], + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + rgb_as_latents=False, + **kwargs, + ): + batch_size = rgb.shape[0] + + rgb_BCHW = rgb.permute(0, 3, 1, 2) + latents: Float[Tensor, "B 4 64 64"] + if rgb_as_latents: + latents = ( + F.interpolate(rgb_BCHW, (32, 32), mode="bilinear", align_corners=False) + * 2 + - 1 + ) + else: + rgb_BCHW_512 = F.interpolate( + rgb_BCHW, (256, 256), mode="bilinear", align_corners=False + ) + # encode image into latents with vae + latents = self.encode_images(rgb_BCHW_512) + + cond = self.get_cond(elevation, azimuth, camera_distances) + + # timestep ~ U(0.02, 0.98) to avoid very high/low noise level + t = torch.randint( + self.min_step, + self.max_step + 1, + [batch_size], + dtype=torch.long, + device=self.device, + ) + + # predict the noise residual with unet, NO grad! + with torch.no_grad(): + # add noise + noise = torch.randn_like(latents) # TODO: use torch generator + latents_noisy = self.scheduler.add_noise(latents, noise, t) + # pred noise + x_in = torch.cat([latents_noisy] * 2) + t_in = torch.cat([t] * 2) + noise_pred = self.model.apply_model(x_in.to(self.weights_dtype), t_in, cond) + + # perform guidance + noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + self.cfg.guidance_scale * ( + noise_pred_cond - noise_pred_uncond + ) + + w = (1 - self.alphas[t]).reshape(-1, 1, 1, 1) + grad = w * (noise_pred - noise) + grad = torch.nan_to_num(grad) + # clip grad for stable training? + if self.grad_clip_val is not None: + grad = grad.clamp(-self.grad_clip_val, self.grad_clip_val) + + # loss = SpecifyGradient.apply(latents, grad) + # SpecifyGradient is not straghtforward, use a reparameterization trick instead + target = (latents - grad).detach() + # d(loss)/d(latents) = latents - target = latents - (latents - grad) = grad + loss_sds = 0.5 * F.mse_loss(latents, target, reduction="sum") / batch_size + + guidance_out = { + "loss_sds": loss_sds, + "grad_norm": grad.norm(), + "min_step": self.min_step, + "max_step": self.max_step, + } + + return guidance_out + + def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False): + # clip grad for stable training as demonstrated in + # Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation + # http://arxiv.org/abs/2303.15413 + if self.cfg.grad_clip is not None: + self.grad_clip_val = C(self.cfg.grad_clip, epoch, global_step) + + self.set_min_max_steps( + min_step_percent=C(self.cfg.min_step_percent, epoch, global_step), + max_step_percent=C(self.cfg.max_step_percent, epoch, global_step), + ) diff --git a/threestudio/models/guidance/zero123_guidance.py b/threestudio/models/guidance/zero123_guidance.py new file mode 100644 index 0000000..87faf68 --- /dev/null +++ b/threestudio/models/guidance/zero123_guidance.py @@ -0,0 +1,512 @@ +import importlib +import os +from dataclasses import dataclass, field + +import cv2 +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from diffusers import DDIMScheduler, DDPMScheduler, StableDiffusionPipeline +from diffusers.utils.import_utils import is_xformers_available +from omegaconf import OmegaConf +from tqdm import tqdm + +import threestudio +from threestudio.utils.base import BaseObject +from threestudio.utils.misc import C, parse_version +from threestudio.utils.typing import * + +import clip + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +def instantiate_from_config(config): + if not "target" in config: + if config == "__is_first_stage__": + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +# load model +def load_model_from_config(config, ckpt, device, vram_O=True, verbose=False): + pl_sd = torch.load(ckpt, map_location="cpu") + + if "global_step" in pl_sd and verbose: + print(f'[INFO] Global Step: {pl_sd["global_step"]}') + + sd = pl_sd["state_dict"] + + model = instantiate_from_config(config.model) + m, u = model.load_state_dict(sd, strict=False) + + if len(m) > 0 and verbose: + print("[INFO] missing keys: \n", m) + if len(u) > 0 and verbose: + print("[INFO] unexpected keys: \n", u) + + # manually load ema and delete it to save GPU memory + if model.use_ema: + if verbose: + print("[INFO] loading EMA...") + model.model_ema.copy_to(model.model) + del model.model_ema + + if vram_O: + # we don't need decoder + del model.first_stage_model.decoder + + torch.cuda.empty_cache() + + model.eval().to(device) + + return model + + +@threestudio.register("zero123-guidance") +class Zero123Guidance(BaseObject): + @dataclass + class Config(BaseObject.Config): + pretrained_model_name_or_path: str = "load/zero123/105000.ckpt" + pretrained_config: str = "load/zero123/sd-objaverse-finetune-c_concat-256.yaml" + vram_O: bool = True + + cond_image_path: str = "load/images/hamburger_rgba.png" + cond_elevation_deg: float = 0.0 + cond_azimuth_deg: float = 0.0 + cond_camera_distance: float = 1.2 + + guidance_scale: float = 5.0 + + grad_clip: Optional[ + Any + ] = None # field(default_factory=lambda: [0, 2.0, 8.0, 1000]) + half_precision_weights: bool = True + + min_step_percent: float = 0.02 + max_step_percent: float = 0.98 + + """Maximum number of batch items to evaluate guidance for (for debugging) and to save on disk. -1 means save all items.""" + max_items_eval: int = 4 + + cfg: Config + + def configure(self) -> None: + threestudio.info(f"Loading Zero123 ...") + + self.config = OmegaConf.load(self.cfg.pretrained_config) + # TODO: seems it cannot load into fp16... + self.weights_dtype = ( + torch.float16 if self.cfg.half_precision_weights else torch.float32 + ) + self.model = load_model_from_config( + self.config, + self.cfg.pretrained_model_name_or_path, + device=self.device, + vram_O=self.cfg.vram_O, + ) + self.model.to(dtype=self.weights_dtype) + # self.model.cond_stage_model.model.visual.ln_pre.to(dtype=torch.float32) + def recursive_layernorm_fp32(model): + for attr in model.__dir__(): + if attr.startswith("_"): + continue + try: + module = getattr(model, attr) + except: + continue # ignore attributes like property, which can't be retrived using getattr? + if isinstance(module, clip.model.LayerNorm): + module.to(dtype=torch.float32) + elif isinstance(module, torch.nn.Sequential): + for sub_module in module: + recursive_layernorm_fp32(sub_module) + elif isinstance(module, torch.nn.Module): + recursive_layernorm_fp32(module) + recursive_layernorm_fp32(self.model) + + for p in self.model.parameters(): + p.requires_grad_(False) + + # timesteps: use diffuser for convenience... hope it's alright. + self.num_train_timesteps = self.config.model.params.timesteps + + self.scheduler = DDIMScheduler( + self.num_train_timesteps, + self.config.model.params.linear_start, + self.config.model.params.linear_end, + beta_schedule="scaled_linear", + clip_sample=False, + set_alpha_to_one=False, + steps_offset=1, + ) + + self.num_train_timesteps = self.scheduler.config.num_train_timesteps + self.set_min_max_steps() # set to default value + + self.alphas: Float[Tensor, "..."] = self.scheduler.alphas_cumprod.to( + self.device + ) + + self.grad_clip_val: Optional[float] = None + + self.prepare_embeddings(self.cfg.cond_image_path) + + threestudio.info(f"Loaded Zero123!") + + @torch.cuda.amp.autocast(enabled=False) + def set_min_max_steps(self, min_step_percent=0.02, max_step_percent=0.98): + self.min_step = int(self.num_train_timesteps * min_step_percent) + self.max_step = int(self.num_train_timesteps * max_step_percent) + + @torch.cuda.amp.autocast(enabled=False) + def prepare_embeddings(self, image_path: str) -> None: + # load cond image for zero123 + assert os.path.exists(image_path) + rgba = cv2.cvtColor( + cv2.imread(image_path, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGRA2RGBA + ) + rgba = ( + cv2.resize(rgba, (256, 256), interpolation=cv2.INTER_AREA).astype( + np.float32 + ) + / 255.0 + ) + rgb = rgba[..., :3] * rgba[..., 3:] + (1 - rgba[..., 3:]) + self.rgb_256: Float[Tensor, "1 3 H W"] = ( + torch.from_numpy(rgb) + .unsqueeze(0) + .permute(0, 3, 1, 2) + .contiguous() + .to(self.device) + ) + self.c_crossattn, self.c_concat = self.get_img_embeds(self.rgb_256) + + @torch.cuda.amp.autocast(enabled=False) + @torch.no_grad() + def get_img_embeds( + self, + img: Float[Tensor, "B 3 256 256"], + ) -> Tuple[Float[Tensor, "B 1 768"], Float[Tensor, "B 4 32 32"]]: + img = img * 2.0 - 1.0 + c_crossattn = self.model.get_learned_conditioning(img.to(self.weights_dtype)) + c_concat = self.model.encode_first_stage(img.to(self.weights_dtype)).mode() + return c_crossattn, c_concat + + @torch.cuda.amp.autocast(enabled=False) + def encode_images( + self, imgs: Float[Tensor, "B 3 256 256"] + ) -> Float[Tensor, "B 4 32 32"]: + input_dtype = imgs.dtype + imgs = imgs * 2.0 - 1.0 + latents = self.model.get_first_stage_encoding( + self.model.encode_first_stage(imgs.to(self.weights_dtype)) + ) + return latents.to(input_dtype) # [B, 4, 32, 32] Latent space image + + @torch.cuda.amp.autocast(enabled=False) + def decode_latents( + self, + latents: Float[Tensor, "B 4 H W"], + ) -> Float[Tensor, "B 3 512 512"]: + input_dtype = latents.dtype + image = self.model.decode_first_stage(latents) + image = (image * 0.5 + 0.5).clamp(0, 1) + return image.to(input_dtype) + + @torch.cuda.amp.autocast(enabled=False) + @torch.no_grad() + def get_cond( + self, + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + c_crossattn=None, + c_concat=None, + **kwargs, + ) -> dict: + T = torch.stack( + [ + torch.deg2rad( + (90 - elevation) - (90 - self.cfg.cond_elevation_deg) + ), # Zero123 polar is 90-elevation + torch.sin(torch.deg2rad(azimuth - self.cfg.cond_azimuth_deg)), + torch.cos(torch.deg2rad(azimuth - self.cfg.cond_azimuth_deg)), + camera_distances - self.cfg.cond_camera_distance, + ], + dim=-1, + )[:, None, :].to(self.device) + cond = {} + clip_emb = self.model.cc_projection( + torch.cat( + [ + (self.c_crossattn if c_crossattn is None else c_crossattn).repeat( + len(T), 1, 1 + ), + T, + ], + dim=-1, + ) + ) + cond["c_crossattn"] = [ + torch.cat([torch.zeros_like(clip_emb).to(self.device), clip_emb], dim=0) + ] + cond["c_concat"] = [ + torch.cat( + [ + torch.zeros_like(self.c_concat) + .repeat(len(T), 1, 1, 1) + .to(self.device), + (self.c_concat if c_concat is None else c_concat).repeat( + len(T), 1, 1, 1 + ), + ], + dim=0, + ) + ] + return cond + + def __call__( + self, + rgb: Float[Tensor, "B H W C"], + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + rgb_as_latents=False, + guidance_eval=False, + **kwargs, + ): + batch_size = rgb.shape[0] + + rgb_BCHW = rgb.permute(0, 3, 1, 2) + latents: Float[Tensor, "B 4 64 64"] + if rgb_as_latents: + latents = ( + F.interpolate(rgb_BCHW, (32, 32), mode="bilinear", align_corners=False) + * 2 + - 1 + ) + else: + rgb_BCHW_512 = F.interpolate( + rgb_BCHW, (256, 256), mode="bilinear", align_corners=False + ) + # encode image into latents with vae + latents = self.encode_images(rgb_BCHW_512) + + cond = self.get_cond(elevation, azimuth, camera_distances) + + # timestep ~ U(0.02, 0.98) to avoid very high/low noise level + t = torch.randint( + self.min_step, + self.max_step + 1, + [batch_size], + dtype=torch.long, + device=self.device, + ) + + # predict the noise residual with unet, NO grad! + with torch.no_grad(): + # add noise + noise = torch.randn_like(latents) # TODO: use torch generator + latents_noisy = self.scheduler.add_noise(latents, noise, t) + # pred noise + x_in = torch.cat([latents_noisy] * 2) + t_in = torch.cat([t] * 2) + noise_pred = self.model.apply_model(x_in, t_in, cond) + + # perform guidance + noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + self.cfg.guidance_scale * ( + noise_pred_cond - noise_pred_uncond + ) + + w = (1 - self.alphas[t]).reshape(-1, 1, 1, 1) + grad = w * (noise_pred - noise) + grad = torch.nan_to_num(grad) + # clip grad for stable training? + if self.grad_clip_val is not None: + grad = grad.clamp(-self.grad_clip_val, self.grad_clip_val) + + # loss = SpecifyGradient.apply(latents, grad) + # SpecifyGradient is not straghtforward, use a reparameterization trick instead + target = (latents - grad).detach() + # d(loss)/d(latents) = latents - target = latents - (latents - grad) = grad + loss_sds = 0.5 * F.mse_loss(latents, target, reduction="sum") / batch_size + + guidance_out = { + "loss_sds": loss_sds, + "grad_norm": grad.norm(), + "min_step": self.min_step, + "max_step": self.max_step, + } + + if guidance_eval: + guidance_eval_utils = { + "cond": cond, + "t_orig": t, + "latents_noisy": latents_noisy, + "noise_pred": noise_pred, + } + guidance_eval_out = self.guidance_eval(**guidance_eval_utils) + texts = [] + for n, e, a, c in zip( + guidance_eval_out["noise_levels"], elevation, azimuth, camera_distances + ): + texts.append( + f"n{n:.02f}\ne{e.item():.01f}\na{a.item():.01f}\nc{c.item():.02f}" + ) + guidance_eval_out.update({"texts": texts}) + guidance_out.update({"eval": guidance_eval_out}) + + return guidance_out + + @torch.cuda.amp.autocast(enabled=False) + @torch.no_grad() + def guidance_eval(self, cond, t_orig, latents_noisy, noise_pred): + # use only 50 timesteps, and find nearest of those to t + self.scheduler.set_timesteps(50) + self.scheduler.timesteps_gpu = self.scheduler.timesteps.to(self.device) + bs = ( + min(self.cfg.max_items_eval, latents_noisy.shape[0]) + if self.cfg.max_items_eval > 0 + else latents_noisy.shape[0] + ) # batch size + large_enough_idxs = self.scheduler.timesteps_gpu.expand([bs, -1]) > t_orig[ + :bs + ].unsqueeze( + -1 + ) # sized [bs,50] > [bs,1] + idxs = torch.min(large_enough_idxs, dim=1)[1] + t = self.scheduler.timesteps_gpu[idxs] + + fracs = list((t / self.scheduler.config.num_train_timesteps).cpu().numpy()) + imgs_noisy = self.decode_latents(latents_noisy[:bs]).permute(0, 2, 3, 1) + + # get prev latent + latents_1step = [] + pred_1orig = [] + for b in range(bs): + step_output = self.scheduler.step( + noise_pred[b : b + 1], t[b], latents_noisy[b : b + 1], eta=1 + ) + latents_1step.append(step_output["prev_sample"]) + pred_1orig.append(step_output["pred_original_sample"]) + latents_1step = torch.cat(latents_1step) + pred_1orig = torch.cat(pred_1orig) + imgs_1step = self.decode_latents(latents_1step).permute(0, 2, 3, 1) + imgs_1orig = self.decode_latents(pred_1orig).permute(0, 2, 3, 1) + + latents_final = [] + for b, i in enumerate(idxs): + latents = latents_1step[b : b + 1] + c = { + "c_crossattn": [cond["c_crossattn"][0][[b, b + len(idxs)], ...]], + "c_concat": [cond["c_concat"][0][[b, b + len(idxs)], ...]], + } + for t in tqdm(self.scheduler.timesteps[i + 1 :], leave=False): + # pred noise + x_in = torch.cat([latents] * 2) + t_in = torch.cat([t.reshape(1)] * 2).to(self.device) + noise_pred = self.model.apply_model(x_in, t_in, c) + # perform guidance + noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + self.cfg.guidance_scale * ( + noise_pred_cond - noise_pred_uncond + ) + # get prev latent + latents = self.scheduler.step(noise_pred, t, latents, eta=1)[ + "prev_sample" + ] + latents_final.append(latents) + + latents_final = torch.cat(latents_final) + imgs_final = self.decode_latents(latents_final).permute(0, 2, 3, 1) + + return { + "bs": bs, + "noise_levels": fracs, + "imgs_noisy": imgs_noisy, + "imgs_1step": imgs_1step, + "imgs_1orig": imgs_1orig, + "imgs_final": imgs_final, + } + + def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False): + # clip grad for stable training as demonstrated in + # Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation + # http://arxiv.org/abs/2303.15413 + if self.cfg.grad_clip is not None: + self.grad_clip_val = C(self.cfg.grad_clip, epoch, global_step) + + self.set_min_max_steps( + min_step_percent=C(self.cfg.min_step_percent, epoch, global_step), + max_step_percent=C(self.cfg.max_step_percent, epoch, global_step), + ) + + # verification - requires `vram_O = False` in load_model_from_config + @torch.no_grad() + def generate( + self, + image, # image tensor [1, 3, H, W] in [0, 1] + elevation=0, + azimuth=0, + camera_distances=0, # new view params + c_crossattn=None, + c_concat=None, + scale=3, + ddim_steps=50, + post_process=True, + ddim_eta=1, + ): + if c_crossattn is None: + c_crossattn, c_concat = self.get_img_embeds(image) + + cond = self.get_cond( + elevation, azimuth, camera_distances, c_crossattn, c_concat + ) + + imgs = self.gen_from_cond(cond, scale, ddim_steps, post_process, ddim_eta) + + return imgs + + # verification - requires `vram_O = False` in load_model_from_config + @torch.no_grad() + def gen_from_cond( + self, + cond, + scale=3, + ddim_steps=50, + post_process=True, + ddim_eta=1, + ): + # produce latents loop + B = cond["c_crossattn"][0].shape[0] // 2 + latents = torch.randn((B, 4, 32, 32), device=self.device) + self.scheduler.set_timesteps(ddim_steps) + + for t in self.scheduler.timesteps: + x_in = torch.cat([latents] * 2) + t_in = torch.cat([t.reshape(1).repeat(B)] * 2).to(self.device) + + noise_pred = self.model.apply_model(x_in, t_in, cond) + noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + scale * ( + noise_pred_cond - noise_pred_uncond + ) + + latents = self.scheduler.step(noise_pred, t, latents, eta=ddim_eta)[ + "prev_sample" + ] + + imgs = self.decode_latents(latents) + imgs = imgs.cpu().numpy().transpose(0, 2, 3, 1) if post_process else imgs + + return imgs diff --git a/threestudio/models/guidance/zero123_unified_guidance.py b/threestudio/models/guidance/zero123_unified_guidance.py new file mode 100644 index 0000000..8274b51 --- /dev/null +++ b/threestudio/models/guidance/zero123_unified_guidance.py @@ -0,0 +1,716 @@ +import os +import random +import sys +from contextlib import contextmanager +from dataclasses import dataclass, field + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.transforms.functional as TF +from diffusers import ( + AutoencoderKL, + DDPMScheduler, + DPMSolverSinglestepScheduler, + UNet2DConditionModel, +) +from diffusers.loaders import AttnProcsLayers +from diffusers.models.attention_processor import LoRAAttnProcessor +from diffusers.models.embeddings import TimestepEmbedding +from PIL import Image +from tqdm import tqdm + +import threestudio +from extern.zero123 import Zero123Pipeline +from threestudio.models.networks import ToDTypeWrapper +from threestudio.models.prompt_processors.base import PromptProcessorOutput +from threestudio.utils.base import BaseModule +from threestudio.utils.misc import C, cleanup, enable_gradient, parse_version +from threestudio.utils.typing import * + + +@threestudio.register("zero123-unified-guidance") +class Zero123UnifiedGuidance(BaseModule): + @dataclass + class Config(BaseModule.Config): + # guidance type, in ["sds", "vsd"] + guidance_type: str = "sds" + + pretrained_model_name_or_path: str = "bennyguo/zero123-diffusers" + guidance_scale: float = 5.0 + weighting_strategy: str = "dreamfusion" + + min_step_percent: Any = 0.02 + max_step_percent: Any = 0.98 + grad_clip: Optional[Any] = None + + return_rgb_1step_orig: bool = False + return_rgb_multistep_orig: bool = False + n_rgb_multistep_orig_steps: int = 4 + + cond_image_path: str = "" + cond_elevation_deg: float = 0.0 + cond_azimuth_deg: float = 0.0 + cond_camera_distance: float = 1.2 + + # efficiency-related configurations + half_precision_weights: bool = True + + # VSD configurations, only used when guidance_type is "vsd" + vsd_phi_model_name_or_path: Optional[str] = None + vsd_guidance_scale_phi: float = 1.0 + vsd_use_lora: bool = True + vsd_lora_cfg_training: bool = False + vsd_lora_n_timestamp_samples: int = 1 + vsd_use_camera_condition: bool = True + # camera condition type, in ["extrinsics", "mvp", "spherical"] + vsd_camera_condition_type: Optional[str] = "extrinsics" + + cfg: Config + + def configure(self) -> None: + self.min_step: Optional[int] = None + self.max_step: Optional[int] = None + self.grad_clip_val: Optional[float] = None + + @dataclass + class NonTrainableModules: + pipe: Zero123Pipeline + pipe_phi: Optional[Zero123Pipeline] = None + + self.weights_dtype = ( + torch.float16 if self.cfg.half_precision_weights else torch.float32 + ) + + threestudio.info(f"Loading Zero123 ...") + + # need to make sure the pipeline file is in path + sys.path.append("extern/") + + pipe_kwargs = { + "safety_checker": None, + "requires_safety_checker": False, + "variant": "fp16" if self.cfg.half_precision_weights else None, + "torch_dtype": self.weights_dtype, + } + pipe = Zero123Pipeline.from_pretrained( + self.cfg.pretrained_model_name_or_path, + **pipe_kwargs, + ).to(self.device) + self.prepare_pipe(pipe) + + # phi network for VSD + # introduce two trainable modules: + # - self.camera_embedding + # - self.lora_layers + pipe_phi = None + + # if the phi network shares the same unet with the pretrain network + # we need to pass additional cross attention kwargs to the unet + self.vsd_share_model = ( + self.cfg.guidance_type == "vsd" + and self.cfg.vsd_phi_model_name_or_path is None + ) + if self.cfg.guidance_type == "vsd": + if self.cfg.vsd_phi_model_name_or_path is None: + pipe_phi = pipe + else: + pipe_phi = Zero123Pipeline.from_pretrained( + self.cfg.vsd_phi_model_name_or_path, + **pipe_kwargs, + ).to(self.device) + self.prepare_pipe(pipe_phi) + + # set up camera embedding + if self.cfg.vsd_use_camera_condition: + if self.cfg.vsd_camera_condition_type in ["extrinsics", "mvp"]: + self.camera_embedding_dim = 16 + elif self.cfg.vsd_camera_condition_type == "spherical": + self.camera_embedding_dim = 4 + else: + raise ValueError("Invalid camera condition type!") + + # FIXME: hard-coded output dim + self.camera_embedding = ToDTypeWrapper( + TimestepEmbedding(self.camera_embedding_dim, 1280), + self.weights_dtype, + ).to(self.device) + pipe_phi.unet.class_embedding = self.camera_embedding + + if self.cfg.vsd_use_lora: + # set up LoRA layers + lora_attn_procs = {} + for name in pipe_phi.unet.attn_processors.keys(): + cross_attention_dim = ( + None + if name.endswith("attn1.processor") + else pipe_phi.unet.config.cross_attention_dim + ) + if name.startswith("mid_block"): + hidden_size = pipe_phi.unet.config.block_out_channels[-1] + elif name.startswith("up_blocks"): + block_id = int(name[len("up_blocks.")]) + hidden_size = list( + reversed(pipe_phi.unet.config.block_out_channels) + )[block_id] + elif name.startswith("down_blocks"): + block_id = int(name[len("down_blocks.")]) + hidden_size = pipe_phi.unet.config.block_out_channels[block_id] + + lora_attn_procs[name] = LoRAAttnProcessor( + hidden_size=hidden_size, cross_attention_dim=cross_attention_dim + ) + + pipe_phi.unet.set_attn_processor(lora_attn_procs) + + self.lora_layers = AttnProcsLayers(pipe_phi.unet.attn_processors).to( + self.device + ) + self.lora_layers._load_state_dict_pre_hooks.clear() + self.lora_layers._state_dict_hooks.clear() + + threestudio.info(f"Loaded Stable Diffusion!") + + self.scheduler = DDPMScheduler.from_config(pipe.scheduler.config) + self.num_train_timesteps = self.scheduler.config.num_train_timesteps + + # q(z_t|x) = N(alpha_t x, sigma_t^2 I) + # in DDPM, alpha_t = sqrt(alphas_cumprod_t), sigma_t^2 = 1 - alphas_cumprod_t + self.alphas_cumprod: Float[Tensor, "T"] = self.scheduler.alphas_cumprod.to( + self.device + ) + self.alphas: Float[Tensor, "T"] = self.alphas_cumprod**0.5 + self.sigmas: Float[Tensor, "T"] = (1 - self.alphas_cumprod) ** 0.5 + # log SNR + self.lambdas: Float[Tensor, "T"] = self.sigmas / self.alphas + + self._non_trainable_modules = NonTrainableModules( + pipe=pipe, + pipe_phi=pipe_phi, + ) + + # self.clip_image_embeddings and self.image_latents + self.prepare_image_embeddings() + + @property + def pipe(self) -> Zero123Pipeline: + return self._non_trainable_modules.pipe + + @property + def pipe_phi(self) -> Zero123Pipeline: + if self._non_trainable_modules.pipe_phi is None: + raise RuntimeError("phi model is not available.") + return self._non_trainable_modules.pipe_phi + + def prepare_pipe(self, pipe: Zero123Pipeline): + cleanup() + + pipe.image_encoder.eval() + pipe.vae.eval() + pipe.unet.eval() + pipe.clip_camera_projection.eval() + + enable_gradient(pipe.image_encoder, enabled=False) + enable_gradient(pipe.vae, enabled=False) + enable_gradient(pipe.unet, enabled=False) + enable_gradient(pipe.clip_camera_projection, enabled=False) + + # disable progress bar + pipe.set_progress_bar_config(disable=True) + + def prepare_image_embeddings(self) -> None: + if not os.path.exists(self.cfg.cond_image_path): + raise RuntimeError( + f"Condition image not found at {self.cfg.cond_image_path}" + ) + image = Image.open(self.cfg.cond_image_path).convert("RGBA").resize((256, 256)) + image = ( + TF.to_tensor(image) + .unsqueeze(0) + .to(device=self.device, dtype=self.weights_dtype) + ) + # rgba -> rgb, apply white background + image = image[:, :3] * image[:, 3:4] + (1 - image[:, 3:4]) + + with torch.no_grad(): + self.clip_image_embeddings: Float[ + Tensor, "1 1 D" + ] = self.extract_clip_image_embeddings(image) + + # encoded latents should be multiplied with vae.config.scaling_factor + # but zero123 was not trained this way + self.image_latents: Float[Tensor, "1 4 Hl Wl"] = ( + self.vae_encode(self.pipe.vae, image * 2.0 - 1.0, mode=True) + / self.pipe.vae.config.scaling_factor + ) + + def extract_clip_image_embeddings( + self, images: Float[Tensor, "B 3 H W"] + ) -> Float[Tensor, "B 1 D"]: + # expect images in [0, 1] + images_pil = [TF.to_pil_image(image) for image in images] + images_processed = self.pipe.feature_extractor( + images=images_pil, return_tensors="pt" + ).pixel_values.to(device=self.device, dtype=self.weights_dtype) + clip_image_embeddings = self.pipe.image_encoder(images_processed).image_embeds + return clip_image_embeddings.to(images.dtype) + + def get_image_camera_embeddings( + self, + elevation_deg: Float[Tensor, "B"], + azimuth_deg: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + ) -> Float[Tensor, "B 1 D"]: + batch_size = elevation_deg.shape[0] + camera_embeddings: Float[Tensor, "B 1 4"] = torch.stack( + [ + torch.deg2rad(self.cfg.cond_elevation_deg - elevation_deg), + torch.sin(torch.deg2rad(azimuth_deg - self.cfg.cond_azimuth_deg)), + torch.cos(torch.deg2rad(azimuth_deg - self.cfg.cond_azimuth_deg)), + camera_distances - self.cfg.cond_camera_distance, + ], + dim=-1, + )[:, None, :] + + image_camera_embeddings = self.pipe.clip_camera_projection( + torch.cat( + [ + self.clip_image_embeddings.repeat(batch_size, 1, 1), + camera_embeddings, + ], + dim=-1, + ).to(self.weights_dtype) + ) + + return image_camera_embeddings + + @torch.cuda.amp.autocast(enabled=False) + def forward_unet( + self, + unet: UNet2DConditionModel, + latents: Float[Tensor, "..."], + t: Int[Tensor, "..."], + encoder_hidden_states: Float[Tensor, "..."], + class_labels: Optional[Float[Tensor, "..."]] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + down_block_additional_residuals: Optional[Float[Tensor, "..."]] = None, + mid_block_additional_residual: Optional[Float[Tensor, "..."]] = None, + velocity_to_epsilon: bool = False, + ) -> Float[Tensor, "..."]: + input_dtype = latents.dtype + pred = unet( + latents.to(unet.dtype), + t.to(unet.dtype), + encoder_hidden_states=encoder_hidden_states.to(unet.dtype), + class_labels=class_labels, + cross_attention_kwargs=cross_attention_kwargs, + down_block_additional_residuals=down_block_additional_residuals, + mid_block_additional_residual=mid_block_additional_residual, + ).sample + if velocity_to_epsilon: + pred = latents * self.sigmas[t].view(-1, 1, 1, 1) + pred * self.alphas[ + t + ].view(-1, 1, 1, 1) + return pred.to(input_dtype) + + @torch.cuda.amp.autocast(enabled=False) + def vae_encode( + self, vae: AutoencoderKL, imgs: Float[Tensor, "B 3 H W"], mode=False + ) -> Float[Tensor, "B 4 Hl Wl"]: + # expect input in [-1, 1] + input_dtype = imgs.dtype + posterior = vae.encode(imgs.to(vae.dtype)).latent_dist + if mode: + latents = posterior.mode() + else: + latents = posterior.sample() + latents = latents * vae.config.scaling_factor + return latents.to(input_dtype) + + @torch.cuda.amp.autocast(enabled=False) + def vae_decode( + self, vae: AutoencoderKL, latents: Float[Tensor, "B 4 Hl Wl"] + ) -> Float[Tensor, "B 3 H W"]: + # output in [0, 1] + input_dtype = latents.dtype + latents = 1 / vae.config.scaling_factor * latents + image = vae.decode(latents.to(vae.dtype)).sample + image = (image * 0.5 + 0.5).clamp(0, 1) + return image.to(input_dtype) + + @contextmanager + def disable_unet_class_embedding(self, unet: UNet2DConditionModel): + class_embedding = unet.class_embedding + try: + unet.class_embedding = None + yield unet + finally: + unet.class_embedding = class_embedding + + @contextmanager + def set_scheduler(self, pipe: Zero123Pipeline, scheduler_class: Any, **kwargs): + scheduler_orig = pipe.scheduler + pipe.scheduler = scheduler_class.from_config(scheduler_orig.config, **kwargs) + yield pipe + pipe.scheduler = scheduler_orig + + def get_eps_pretrain( + self, + latents_noisy: Float[Tensor, "B 4 Hl Wl"], + t: Int[Tensor, "B"], + image_camera_embeddings: Float[Tensor, "B 1 D"], + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + ) -> Float[Tensor, "B 4 Hl Wl"]: + batch_size = latents_noisy.shape[0] + + with torch.no_grad(): + with self.disable_unet_class_embedding(self.pipe.unet) as unet: + noise_pred = self.forward_unet( + unet, + torch.cat( + [ + torch.cat([latents_noisy] * 2, dim=0), + torch.cat( + [ + self.image_latents.repeat(batch_size, 1, 1, 1), + torch.zeros_like(self.image_latents).repeat( + batch_size, 1, 1, 1 + ), + ], + dim=0, + ), + ], + dim=1, + ), + torch.cat([t] * 2, dim=0), + encoder_hidden_states=torch.cat( + [ + image_camera_embeddings, + torch.zeros_like(image_camera_embeddings), + ], + dim=0, + ), + cross_attention_kwargs={"scale": 0.0} + if self.vsd_share_model + else None, + velocity_to_epsilon=self.pipe.scheduler.config.prediction_type + == "v_prediction", + ) + + noise_pred_image, noise_pred_uncond = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + self.cfg.guidance_scale * ( + noise_pred_image - noise_pred_uncond + ) + + return noise_pred + + def get_eps_phi( + self, + latents_noisy: Float[Tensor, "B 4 Hl Wl"], + t: Int[Tensor, "B"], + image_camera_embeddings: Float[Tensor, "B 1 D"], + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + camera_condition: Float[Tensor, "B ..."], + ) -> Float[Tensor, "B 4 Hl Wl"]: + batch_size = latents_noisy.shape[0] + + with torch.no_grad(): + noise_pred = self.forward_unet( + self.pipe_phi.unet, + torch.cat( + [ + torch.cat([latents_noisy] * 2, dim=0), + torch.cat( + [self.image_latents.repeat(batch_size, 1, 1, 1)] * 2, + dim=0, + ), + ], + dim=1, + ), + torch.cat([t] * 2, dim=0), + encoder_hidden_states=torch.cat([image_camera_embeddings] * 2, dim=0), + class_labels=torch.cat( + [ + camera_condition.view(batch_size, -1), + torch.zeros_like(camera_condition.view(batch_size, -1)), + ], + dim=0, + ) + if self.cfg.vsd_use_camera_condition + else None, + cross_attention_kwargs={"scale": 1.0}, + velocity_to_epsilon=self.pipe_phi.scheduler.config.prediction_type + == "v_prediction", + ) + + noise_pred_camera, noise_pred_uncond = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + self.cfg.vsd_guidance_scale_phi * ( + noise_pred_camera - noise_pred_uncond + ) + + return noise_pred + + def train_phi( + self, + latents: Float[Tensor, "B 4 Hl Wl"], + image_camera_embeddings: Float[Tensor, "B 1 D"], + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + camera_condition: Float[Tensor, "B ..."], + ): + B = latents.shape[0] + latents = latents.detach().repeat( + self.cfg.vsd_lora_n_timestamp_samples, 1, 1, 1 + ) + + num_train_timesteps = self.pipe_phi.scheduler.config.num_train_timesteps + t = torch.randint( + int(num_train_timesteps * 0.0), + int(num_train_timesteps * 1.0), + [B * self.cfg.vsd_lora_n_timestamp_samples], + dtype=torch.long, + device=self.device, + ) + + noise = torch.randn_like(latents) + latents_noisy = self.pipe_phi.scheduler.add_noise(latents, noise, t) + if self.pipe_phi.scheduler.config.prediction_type == "epsilon": + target = noise + elif self.pipe_phi.scheduler.prediction_type == "v_prediction": + target = self.pipe_phi.scheduler.get_velocity(latents, noise, t) + else: + raise ValueError( + f"Unknown prediction type {self.pipe_phi.scheduler.prediction_type}" + ) + + if ( + self.cfg.vsd_use_camera_condition + and self.cfg.vsd_lora_cfg_training + and random.random() < 0.1 + ): + camera_condition = torch.zeros_like(camera_condition) + + noise_pred = self.forward_unet( + self.pipe_phi.unet, + torch.cat([latents_noisy, self.image_latents.repeat(B, 1, 1, 1)], dim=1), + t, + encoder_hidden_states=image_camera_embeddings.repeat( + self.cfg.vsd_lora_n_timestamp_samples, 1, 1 + ), + class_labels=camera_condition.view(B, -1).repeat( + self.cfg.vsd_lora_n_timestamp_samples, 1 + ) + if self.cfg.vsd_use_camera_condition + else None, + cross_attention_kwargs={"scale": 1.0}, + ) + return F.mse_loss(noise_pred.float(), target.float(), reduction="mean") + + def forward( + self, + rgb: Float[Tensor, "B H W C"], + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + mvp_mtx: Float[Tensor, "B 4 4"], + c2w: Float[Tensor, "B 4 4"], + rgb_as_latents=False, + **kwargs, + ): + batch_size = rgb.shape[0] + + rgb_BCHW = rgb.permute(0, 3, 1, 2) + latents: Float[Tensor, "B 4 32 32"] + if rgb_as_latents: + # treat input rgb as latents + # input rgb should be in range [-1, 1] + latents = F.interpolate( + rgb_BCHW, (32, 32), mode="bilinear", align_corners=False + ) + else: + # treat input rgb as rgb + # input rgb should be in range [0, 1] + rgb_BCHW = F.interpolate( + rgb_BCHW, (256, 256), mode="bilinear", align_corners=False + ) + # encode image into latents with vae + latents = self.vae_encode(self.pipe.vae, rgb_BCHW * 2.0 - 1.0) + + # sample timestep + # use the same timestep for each batch + assert self.min_step is not None and self.max_step is not None + t = torch.randint( + self.min_step, + self.max_step + 1, + [1], + dtype=torch.long, + device=self.device, + ).repeat(batch_size) + + # sample noise + noise = torch.randn_like(latents) + latents_noisy = self.scheduler.add_noise(latents, noise, t) + + # image-camera feature condition + image_camera_embeddings = self.get_image_camera_embeddings( + elevation, azimuth, camera_distances + ) + + eps_pretrain = self.get_eps_pretrain( + latents_noisy, + t, + image_camera_embeddings, + elevation, + azimuth, + camera_distances, + ) + + latents_1step_orig = ( + 1 + / self.alphas[t].view(-1, 1, 1, 1) + * (latents_noisy - self.sigmas[t].view(-1, 1, 1, 1) * eps_pretrain) + ).detach() + + if self.cfg.guidance_type == "sds": + eps_phi = noise + elif self.cfg.guidance_type == "vsd": + if self.cfg.vsd_camera_condition_type == "extrinsics": + camera_condition = c2w + elif self.cfg.vsd_camera_condition_type == "mvp": + camera_condition = mvp_mtx + elif self.cfg.vsd_camera_condition_type == "spherical": + camera_condition = torch.stack( + [ + torch.deg2rad(elevation), + torch.sin(torch.deg2rad(azimuth)), + torch.cos(torch.deg2rad(azimuth)), + camera_distances, + ], + dim=-1, + ) + else: + raise ValueError( + f"Unknown camera_condition_type {self.cfg.vsd_camera_condition_type}" + ) + eps_phi = self.get_eps_phi( + latents_noisy, + t, + image_camera_embeddings, + elevation, + azimuth, + camera_distances, + camera_condition, + ) + + loss_train_phi = self.train_phi( + latents, + image_camera_embeddings, + elevation, + azimuth, + camera_distances, + camera_condition, + ) + + if self.cfg.weighting_strategy == "dreamfusion": + w = (1.0 - self.alphas[t]).view(-1, 1, 1, 1) + elif self.cfg.weighting_strategy == "uniform": + w = 1.0 + elif self.cfg.weighting_strategy == "fantasia3d": + w = (self.alphas[t] ** 0.5 * (1 - self.alphas[t])).view(-1, 1, 1, 1) + else: + raise ValueError( + f"Unknown weighting strategy: {self.cfg.weighting_strategy}" + ) + + grad = w * (eps_pretrain - eps_phi) + + if self.grad_clip_val is not None: + grad = grad.clamp(-self.grad_clip_val, self.grad_clip_val) + + # reparameterization trick: + # d(loss)/d(latents) = latents - target = latents - (latents - grad) = grad + target = (latents - grad).detach() + loss_sd = 0.5 * F.mse_loss(latents, target, reduction="sum") / batch_size + + guidance_out = { + "loss_sd": loss_sd, + "grad_norm": grad.norm(), + "timesteps": t, + "min_step": self.min_step, + "max_step": self.max_step, + "latents": latents, + "latents_1step_orig": latents_1step_orig, + "rgb": rgb_BCHW.permute(0, 2, 3, 1), + "weights": w, + "lambdas": self.lambdas[t], + } + + if self.cfg.return_rgb_1step_orig: + with torch.no_grad(): + rgb_1step_orig = self.vae_decode( + self.pipe.vae, latents_1step_orig + ).permute(0, 2, 3, 1) + guidance_out.update({"rgb_1step_orig": rgb_1step_orig}) + + if self.cfg.return_rgb_multistep_orig: + with self.set_scheduler( + self.pipe, + DPMSolverSinglestepScheduler, + solver_order=1, + num_train_timesteps=int(t[0]), + ) as pipe: + with torch.cuda.amp.autocast(enabled=False): + latents_multistep_orig = pipe( + num_inference_steps=self.cfg.n_rgb_multistep_orig_steps, + guidance_scale=self.cfg.guidance_scale, + eta=1.0, + latents=latents_noisy.to(pipe.unet.dtype), + image_camera_embeddings=image_camera_embeddings.to( + pipe.unet.dtype + ), + image_latents=self.image_latents.repeat(batch_size, 1, 1, 1).to( + pipe.unet.dtype + ), + cross_attention_kwargs={"scale": 0.0} + if self.vsd_share_model + else None, + output_type="latent", + ).images.to(latents.dtype) + with torch.no_grad(): + rgb_multistep_orig = self.vae_decode( + self.pipe.vae, latents_multistep_orig + ) + guidance_out.update( + { + "latents_multistep_orig": latents_multistep_orig, + "rgb_multistep_orig": rgb_multistep_orig.permute(0, 2, 3, 1), + } + ) + + if self.cfg.guidance_type == "vsd": + guidance_out.update( + { + "loss_train_phi": loss_train_phi, + } + ) + + return guidance_out + + def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False): + # clip grad for stable training as demonstrated in + # Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation + # http://arxiv.org/abs/2303.15413 + if self.cfg.grad_clip is not None: + self.grad_clip_val = C(self.cfg.grad_clip, epoch, global_step) + + self.min_step = int( + self.num_train_timesteps * C(self.cfg.min_step_percent, epoch, global_step) + ) + self.max_step = int( + self.num_train_timesteps * C(self.cfg.max_step_percent, epoch, global_step) + ) diff --git a/threestudio/models/isosurface.py b/threestudio/models/isosurface.py new file mode 100644 index 0000000..3a7f149 --- /dev/null +++ b/threestudio/models/isosurface.py @@ -0,0 +1,253 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.models.mesh import Mesh +from threestudio.utils.typing import * + + +class IsosurfaceHelper(nn.Module): + points_range: Tuple[float, float] = (0, 1) + + @property + def grid_vertices(self) -> Float[Tensor, "N 3"]: + raise NotImplementedError + + +class MarchingCubeCPUHelper(IsosurfaceHelper): + def __init__(self, resolution: int) -> None: + super().__init__() + self.resolution = resolution + import mcubes + + self.mc_func: Callable = mcubes.marching_cubes + self._grid_vertices: Optional[Float[Tensor, "N3 3"]] = None + self._dummy: Float[Tensor, "..."] + self.register_buffer( + "_dummy", torch.zeros(0, dtype=torch.float32), persistent=False + ) + + @property + def grid_vertices(self) -> Float[Tensor, "N3 3"]: + if self._grid_vertices is None: + # keep the vertices on CPU so that we can support very large resolution + x, y, z = ( + torch.linspace(*self.points_range, self.resolution), + torch.linspace(*self.points_range, self.resolution), + torch.linspace(*self.points_range, self.resolution), + ) + x, y, z = torch.meshgrid(x, y, z, indexing="ij") + verts = torch.cat( + [x.reshape(-1, 1), y.reshape(-1, 1), z.reshape(-1, 1)], dim=-1 + ).reshape(-1, 3) + self._grid_vertices = verts + return self._grid_vertices + + def forward( + self, + level: Float[Tensor, "N3 1"], + deformation: Optional[Float[Tensor, "N3 3"]] = None, + ) -> Mesh: + if deformation is not None: + threestudio.warn( + f"{self.__class__.__name__} does not support deformation. Ignoring." + ) + level = -level.view(self.resolution, self.resolution, self.resolution) + v_pos, t_pos_idx = self.mc_func( + level.detach().cpu().numpy(), 0.0 + ) # transform to numpy + v_pos, t_pos_idx = ( + torch.from_numpy(v_pos).float().to(self._dummy.device), + torch.from_numpy(t_pos_idx.astype(np.int64)).long().to(self._dummy.device), + ) # transform back to torch tensor on CUDA + v_pos = v_pos / (self.resolution - 1.0) + return Mesh(v_pos=v_pos, t_pos_idx=t_pos_idx) + + +class MarchingTetrahedraHelper(IsosurfaceHelper): + def __init__(self, resolution: int, tets_path: str): + super().__init__() + self.resolution = resolution + self.tets_path = tets_path + + self.triangle_table: Float[Tensor, "..."] + self.register_buffer( + "triangle_table", + torch.as_tensor( + [ + [-1, -1, -1, -1, -1, -1], + [1, 0, 2, -1, -1, -1], + [4, 0, 3, -1, -1, -1], + [1, 4, 2, 1, 3, 4], + [3, 1, 5, -1, -1, -1], + [2, 3, 0, 2, 5, 3], + [1, 4, 0, 1, 5, 4], + [4, 2, 5, -1, -1, -1], + [4, 5, 2, -1, -1, -1], + [4, 1, 0, 4, 5, 1], + [3, 2, 0, 3, 5, 2], + [1, 3, 5, -1, -1, -1], + [4, 1, 2, 4, 3, 1], + [3, 0, 4, -1, -1, -1], + [2, 0, 1, -1, -1, -1], + [-1, -1, -1, -1, -1, -1], + ], + dtype=torch.long, + ), + persistent=False, + ) + self.num_triangles_table: Integer[Tensor, "..."] + self.register_buffer( + "num_triangles_table", + torch.as_tensor( + [0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long + ), + persistent=False, + ) + self.base_tet_edges: Integer[Tensor, "..."] + self.register_buffer( + "base_tet_edges", + torch.as_tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long), + persistent=False, + ) + + tets = np.load(self.tets_path) + self._grid_vertices: Float[Tensor, "..."] + self.register_buffer( + "_grid_vertices", + torch.from_numpy(tets["vertices"]).float(), + persistent=False, + ) + self.indices: Integer[Tensor, "..."] + self.register_buffer( + "indices", torch.from_numpy(tets["indices"]).long(), persistent=False + ) + + self._all_edges: Optional[Integer[Tensor, "Ne 2"]] = None + + def normalize_grid_deformation( + self, grid_vertex_offsets: Float[Tensor, "Nv 3"] + ) -> Float[Tensor, "Nv 3"]: + return ( + (self.points_range[1] - self.points_range[0]) + / (self.resolution) # half tet size is approximately 1 / self.resolution + * torch.tanh(grid_vertex_offsets) + ) # FIXME: hard-coded activation + + @property + def grid_vertices(self) -> Float[Tensor, "Nv 3"]: + return self._grid_vertices + + @property + def all_edges(self) -> Integer[Tensor, "Ne 2"]: + if self._all_edges is None: + # compute edges on GPU, or it would be VERY SLOW (basically due to the unique operation) + edges = torch.tensor( + [0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], + dtype=torch.long, + device=self.indices.device, + ) + _all_edges = self.indices[:, edges].reshape(-1, 2) + _all_edges_sorted = torch.sort(_all_edges, dim=1)[0] + _all_edges = torch.unique(_all_edges_sorted, dim=0) + self._all_edges = _all_edges + return self._all_edges + + def sort_edges(self, edges_ex2): + with torch.no_grad(): + order = (edges_ex2[:, 0] > edges_ex2[:, 1]).long() + order = order.unsqueeze(dim=1) + + a = torch.gather(input=edges_ex2, index=order, dim=1) + b = torch.gather(input=edges_ex2, index=1 - order, dim=1) + + return torch.stack([a, b], -1) + + def _forward(self, pos_nx3, sdf_n, tet_fx4): + with torch.no_grad(): + occ_n = sdf_n > 0 + occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4) + occ_sum = torch.sum(occ_fx4, -1) + valid_tets = (occ_sum > 0) & (occ_sum < 4) + occ_sum = occ_sum[valid_tets] + + # find all vertices + all_edges = tet_fx4[valid_tets][:, self.base_tet_edges].reshape(-1, 2) + all_edges = self.sort_edges(all_edges) + unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True) + + unique_edges = unique_edges.long() + mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1 + mapping = ( + torch.ones( + (unique_edges.shape[0]), dtype=torch.long, device=pos_nx3.device + ) + * -1 + ) + mapping[mask_edges] = torch.arange( + mask_edges.sum(), dtype=torch.long, device=pos_nx3.device + ) + idx_map = mapping[idx_map] # map edges to verts + + interp_v = unique_edges[mask_edges] + edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3) + edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1) + edges_to_interp_sdf[:, -1] *= -1 + + denominator = edges_to_interp_sdf.sum(1, keepdim=True) + + edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator + verts = (edges_to_interp * edges_to_interp_sdf).sum(1) + + idx_map = idx_map.reshape(-1, 6) + + v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=pos_nx3.device)) + tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1) + num_triangles = self.num_triangles_table[tetindex] + + # Generate triangle indices + faces = torch.cat( + ( + torch.gather( + input=idx_map[num_triangles == 1], + dim=1, + index=self.triangle_table[tetindex[num_triangles == 1]][:, :3], + ).reshape(-1, 3), + torch.gather( + input=idx_map[num_triangles == 2], + dim=1, + index=self.triangle_table[tetindex[num_triangles == 2]][:, :6], + ).reshape(-1, 3), + ), + dim=0, + ) + + return verts, faces + + def forward( + self, + level: Float[Tensor, "N3 1"], + deformation: Optional[Float[Tensor, "N3 3"]] = None, + ) -> Mesh: + if deformation is not None: + grid_vertices = self.grid_vertices + self.normalize_grid_deformation( + deformation + ) + else: + grid_vertices = self.grid_vertices + + v_pos, t_pos_idx = self._forward(grid_vertices, level, self.indices) + + mesh = Mesh( + v_pos=v_pos, + t_pos_idx=t_pos_idx, + # extras + grid_vertices=grid_vertices, + tet_edges=self.all_edges, + grid_level=level, + grid_deformation=deformation, + ) + + return mesh diff --git a/threestudio/models/materials/__init__.py b/threestudio/models/materials/__init__.py new file mode 100644 index 0000000..85d50ba --- /dev/null +++ b/threestudio/models/materials/__init__.py @@ -0,0 +1,9 @@ +from . import ( + base, + diffuse_with_point_light_material, + hybrid_rgb_latent_material, + neural_radiance_material, + no_material, + pbr_material, + sd_latent_adapter_material, +) diff --git a/threestudio/models/materials/base.py b/threestudio/models/materials/base.py new file mode 100644 index 0000000..9df8f30 --- /dev/null +++ b/threestudio/models/materials/base.py @@ -0,0 +1,29 @@ +import random +from dataclasses import dataclass, field + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.utils.base import BaseModule +from threestudio.utils.typing import * + + +class BaseMaterial(BaseModule): + @dataclass + class Config(BaseModule.Config): + pass + + cfg: Config + requires_normal: bool = False + requires_tangent: bool = False + + def configure(self): + pass + + def forward(self, *args, **kwargs) -> Float[Tensor, "*B 3"]: + raise NotImplementedError + + def export(self, *args, **kwargs) -> Dict[str, Any]: + return {} diff --git a/threestudio/models/materials/diffuse_with_point_light_material.py b/threestudio/models/materials/diffuse_with_point_light_material.py new file mode 100644 index 0000000..abf0671 --- /dev/null +++ b/threestudio/models/materials/diffuse_with_point_light_material.py @@ -0,0 +1,120 @@ +import random +from dataclasses import dataclass, field + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.models.materials.base import BaseMaterial +from threestudio.utils.ops import dot, get_activation +from threestudio.utils.typing import * + + +@threestudio.register("diffuse-with-point-light-material") +class DiffuseWithPointLightMaterial(BaseMaterial): + @dataclass + class Config(BaseMaterial.Config): + ambient_light_color: Tuple[float, float, float] = (0.1, 0.1, 0.1) + diffuse_light_color: Tuple[float, float, float] = (0.9, 0.9, 0.9) + ambient_only_steps: int = 1000 + diffuse_prob: float = 0.75 + textureless_prob: float = 0.5 + albedo_activation: str = "sigmoid" + soft_shading: bool = False + + cfg: Config + + def configure(self) -> None: + self.requires_normal = True + + self.ambient_light_color: Float[Tensor, "3"] + self.register_buffer( + "ambient_light_color", + torch.as_tensor(self.cfg.ambient_light_color, dtype=torch.float32), + ) + self.diffuse_light_color: Float[Tensor, "3"] + self.register_buffer( + "diffuse_light_color", + torch.as_tensor(self.cfg.diffuse_light_color, dtype=torch.float32), + ) + self.ambient_only = False + + def forward( + self, + features: Float[Tensor, "B ... Nf"], + positions: Float[Tensor, "B ... 3"], + shading_normal: Float[Tensor, "B ... 3"], + light_positions: Float[Tensor, "B ... 3"], + ambient_ratio: Optional[float] = None, + shading: Optional[str] = None, + **kwargs, + ) -> Float[Tensor, "B ... 3"]: + albedo = get_activation(self.cfg.albedo_activation)(features[..., :3]) + + if ambient_ratio is not None: + # if ambient ratio is specified, use it + diffuse_light_color = (1 - ambient_ratio) * torch.ones_like( + self.diffuse_light_color + ) + ambient_light_color = ambient_ratio * torch.ones_like( + self.ambient_light_color + ) + elif self.training and self.cfg.soft_shading: + # otherwise if in training and soft shading is enabled, random a ambient ratio + diffuse_light_color = torch.full_like( + self.diffuse_light_color, random.random() + ) + ambient_light_color = 1.0 - diffuse_light_color + else: + # otherwise use the default fixed values + diffuse_light_color = self.diffuse_light_color + ambient_light_color = self.ambient_light_color + + light_directions: Float[Tensor, "B ... 3"] = F.normalize( + light_positions - positions, dim=-1 + ) + diffuse_light: Float[Tensor, "B ... 3"] = ( + dot(shading_normal, light_directions).clamp(min=0.0) * diffuse_light_color + ) + textureless_color = diffuse_light + ambient_light_color + # clamp albedo to [0, 1] to compute shading + color = albedo.clamp(0.0, 1.0) * textureless_color + + if shading is None: + if self.training: + # adopt the same type of augmentation for the whole batch + if self.ambient_only or random.random() > self.cfg.diffuse_prob: + shading = "albedo" + elif random.random() < self.cfg.textureless_prob: + shading = "textureless" + else: + shading = "diffuse" + else: + if self.ambient_only: + shading = "albedo" + else: + # return shaded color by default in evaluation + shading = "diffuse" + + # multiply by 0 to prevent checking for unused parameters in DDP + if shading == "albedo": + return albedo + textureless_color * 0 + elif shading == "textureless": + return albedo * 0 + textureless_color + elif shading == "diffuse": + return color + else: + raise ValueError(f"Unknown shading type {shading}") + + def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False): + if global_step < self.cfg.ambient_only_steps: + self.ambient_only = True + else: + self.ambient_only = False + + def export(self, features: Float[Tensor, "*N Nf"], **kwargs) -> Dict[str, Any]: + albedo = get_activation(self.cfg.albedo_activation)(features[..., :3]).clamp( + 0.0, 1.0 + ) + return {"albedo": albedo} diff --git a/threestudio/models/materials/hybrid_rgb_latent_material.py b/threestudio/models/materials/hybrid_rgb_latent_material.py new file mode 100644 index 0000000..f5f2c51 --- /dev/null +++ b/threestudio/models/materials/hybrid_rgb_latent_material.py @@ -0,0 +1,36 @@ +import random +from dataclasses import dataclass, field + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.models.materials.base import BaseMaterial +from threestudio.models.networks import get_encoding, get_mlp +from threestudio.utils.ops import dot, get_activation +from threestudio.utils.typing import * + + +@threestudio.register("hybrid-rgb-latent-material") +class HybridRGBLatentMaterial(BaseMaterial): + @dataclass + class Config(BaseMaterial.Config): + n_output_dims: int = 3 + color_activation: str = "sigmoid" + requires_normal: bool = True + + cfg: Config + + def configure(self) -> None: + self.requires_normal = self.cfg.requires_normal + + def forward( + self, features: Float[Tensor, "B ... Nf"], **kwargs + ) -> Float[Tensor, "B ... Nc"]: + assert ( + features.shape[-1] == self.cfg.n_output_dims + ), f"Expected {self.cfg.n_output_dims} output dims, only got {features.shape[-1]} dims input." + color = features + color[..., :3] = get_activation(self.cfg.color_activation)(color[..., :3]) + return color diff --git a/threestudio/models/materials/neural_radiance_material.py b/threestudio/models/materials/neural_radiance_material.py new file mode 100644 index 0000000..c9dcc50 --- /dev/null +++ b/threestudio/models/materials/neural_radiance_material.py @@ -0,0 +1,54 @@ +import random +from dataclasses import dataclass, field + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.models.materials.base import BaseMaterial +from threestudio.models.networks import get_encoding, get_mlp +from threestudio.utils.ops import dot, get_activation +from threestudio.utils.typing import * + + +@threestudio.register("neural-radiance-material") +class NeuralRadianceMaterial(BaseMaterial): + @dataclass + class Config(BaseMaterial.Config): + input_feature_dims: int = 8 + color_activation: str = "sigmoid" + dir_encoding_config: dict = field( + default_factory=lambda: {"otype": "SphericalHarmonics", "degree": 3} + ) + mlp_network_config: dict = field( + default_factory=lambda: { + "otype": "FullyFusedMLP", + "activation": "ReLU", + "n_neurons": 16, + "n_hidden_layers": 2, + } + ) + + cfg: Config + + def configure(self) -> None: + self.encoding = get_encoding(3, self.cfg.dir_encoding_config) + self.n_input_dims = self.cfg.input_feature_dims + self.encoding.n_output_dims # type: ignore + self.network = get_mlp(self.n_input_dims, 3, self.cfg.mlp_network_config) + + def forward( + self, + features: Float[Tensor, "*B Nf"], + viewdirs: Float[Tensor, "*B 3"], + **kwargs, + ) -> Float[Tensor, "*B 3"]: + # viewdirs and normals must be normalized before passing to this function + viewdirs = (viewdirs + 1.0) / 2.0 # (-1, 1) => (0, 1) + viewdirs_embd = self.encoding(viewdirs.view(-1, 3)) + network_inp = torch.cat( + [features.view(-1, features.shape[-1]), viewdirs_embd], dim=-1 + ) + color = self.network(network_inp).view(*features.shape[:-1], 3) + color = get_activation(self.cfg.color_activation)(color) + return color diff --git a/threestudio/models/materials/no_material.py b/threestudio/models/materials/no_material.py new file mode 100644 index 0000000..402a951 --- /dev/null +++ b/threestudio/models/materials/no_material.py @@ -0,0 +1,63 @@ +import random +from dataclasses import dataclass, field + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.models.materials.base import BaseMaterial +from threestudio.models.networks import get_encoding, get_mlp +from threestudio.utils.ops import dot, get_activation +from threestudio.utils.typing import * + + +@threestudio.register("no-material") +class NoMaterial(BaseMaterial): + @dataclass + class Config(BaseMaterial.Config): + n_output_dims: int = 3 + color_activation: str = "sigmoid" + input_feature_dims: Optional[int] = None + mlp_network_config: Optional[dict] = None + requires_normal: bool = False + + cfg: Config + + def configure(self) -> None: + self.use_network = False + if ( + self.cfg.input_feature_dims is not None + and self.cfg.mlp_network_config is not None + ): + self.network = get_mlp( + self.cfg.input_feature_dims, + self.cfg.n_output_dims, + self.cfg.mlp_network_config, + ) + self.use_network = True + self.requires_normal = self.cfg.requires_normal + + def forward( + self, features: Float[Tensor, "B ... Nf"], **kwargs + ) -> Float[Tensor, "B ... Nc"]: + if not self.use_network: + assert ( + features.shape[-1] == self.cfg.n_output_dims + ), f"Expected {self.cfg.n_output_dims} output dims, only got {features.shape[-1]} dims input." + color = get_activation(self.cfg.color_activation)(features) + else: + color = self.network(features.view(-1, features.shape[-1])).view( + *features.shape[:-1], self.cfg.n_output_dims + ) + color = get_activation(self.cfg.color_activation)(color) + return color + + def export(self, features: Float[Tensor, "*N Nf"], **kwargs) -> Dict[str, Any]: + color = self(features, **kwargs).clamp(0, 1) + assert color.shape[-1] >= 3, "Output color must have at least 3 channels" + if color.shape[-1] > 3: + threestudio.warn( + "Output color has >3 channels, treating the first 3 as RGB" + ) + return {"albedo": color[..., :3]} diff --git a/threestudio/models/materials/pbr_material.py b/threestudio/models/materials/pbr_material.py new file mode 100644 index 0000000..c81f67b --- /dev/null +++ b/threestudio/models/materials/pbr_material.py @@ -0,0 +1,143 @@ +import random +from dataclasses import dataclass, field + +import envlight +import numpy as np +import nvdiffrast.torch as dr +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.models.materials.base import BaseMaterial +from threestudio.utils.ops import get_activation +from threestudio.utils.typing import * + + +@threestudio.register("pbr-material") +class PBRMaterial(BaseMaterial): + @dataclass + class Config(BaseMaterial.Config): + material_activation: str = "sigmoid" + environment_texture: str = "load/lights/mud_road_puresky_1k.hdr" + environment_scale: float = 2.0 + min_metallic: float = 0.0 + max_metallic: float = 0.9 + min_roughness: float = 0.08 + max_roughness: float = 0.9 + use_bump: bool = True + + cfg: Config + + def configure(self) -> None: + self.requires_normal = True + self.requires_tangent = self.cfg.use_bump + + self.light = envlight.EnvLight( + self.cfg.environment_texture, scale=self.cfg.environment_scale + ) + + FG_LUT = torch.from_numpy( + np.fromfile("load/lights/bsdf_256_256.bin", dtype=np.float32).reshape( + 1, 256, 256, 2 + ) + ) + self.register_buffer("FG_LUT", FG_LUT) + + def forward( + self, + features: Float[Tensor, "*B Nf"], + viewdirs: Float[Tensor, "*B 3"], + shading_normal: Float[Tensor, "B ... 3"], + tangent: Optional[Float[Tensor, "B ... 3"]] = None, + **kwargs, + ) -> Float[Tensor, "*B 3"]: + prefix_shape = features.shape[:-1] + + material: Float[Tensor, "*B Nf"] = get_activation(self.cfg.material_activation)( + features + ) + albedo = material[..., :3] + metallic = ( + material[..., 3:4] * (self.cfg.max_metallic - self.cfg.min_metallic) + + self.cfg.min_metallic + ) + roughness = ( + material[..., 4:5] * (self.cfg.max_roughness - self.cfg.min_roughness) + + self.cfg.min_roughness + ) + + if self.cfg.use_bump: + assert tangent is not None + # perturb_normal is a delta to the initialization [0, 0, 1] + perturb_normal = (material[..., 5:8] * 2 - 1) + torch.tensor( + [0, 0, 1], dtype=material.dtype, device=material.device + ) + perturb_normal = F.normalize(perturb_normal.clamp(-1, 1), dim=-1) + + # apply normal perturbation in tangent space + bitangent = F.normalize(torch.cross(tangent, shading_normal), dim=-1) + shading_normal = ( + tangent * perturb_normal[..., 0:1] + - bitangent * perturb_normal[..., 1:2] + + shading_normal * perturb_normal[..., 2:3] + ) + shading_normal = F.normalize(shading_normal, dim=-1) + + v = -viewdirs + n_dot_v = (shading_normal * v).sum(-1, keepdim=True) + reflective = n_dot_v * shading_normal * 2 - v + + diffuse_albedo = (1 - metallic) * albedo + + fg_uv = torch.cat([n_dot_v, roughness], -1).clamp(0, 1) + fg = dr.texture( + self.FG_LUT, + fg_uv.reshape(1, -1, 1, 2).contiguous(), + filter_mode="linear", + boundary_mode="clamp", + ).reshape(*prefix_shape, 2) + F0 = (1 - metallic) * 0.04 + metallic * albedo + specular_albedo = F0 * fg[:, 0:1] + fg[:, 1:2] + + diffuse_light = self.light(shading_normal) + specular_light = self.light(reflective, roughness) + + color = diffuse_albedo * diffuse_light + specular_albedo * specular_light + color = color.clamp(0.0, 1.0) + + return color + + def export(self, features: Float[Tensor, "*N Nf"], **kwargs) -> Dict[str, Any]: + material: Float[Tensor, "*N Nf"] = get_activation(self.cfg.material_activation)( + features + ) + albedo = material[..., :3] + metallic = ( + material[..., 3:4] * (self.cfg.max_metallic - self.cfg.min_metallic) + + self.cfg.min_metallic + ) + roughness = ( + material[..., 4:5] * (self.cfg.max_roughness - self.cfg.min_roughness) + + self.cfg.min_roughness + ) + + out = { + "albedo": albedo, + "metallic": metallic, + "roughness": roughness, + } + + if self.cfg.use_bump: + perturb_normal = (material[..., 5:8] * 2 - 1) + torch.tensor( + [0, 0, 1], dtype=material.dtype, device=material.device + ) + perturb_normal = F.normalize(perturb_normal.clamp(-1, 1), dim=-1) + perturb_normal = (perturb_normal + 1) / 2 + out.update( + { + "bump": perturb_normal, + } + ) + + return out diff --git a/threestudio/models/materials/sd_latent_adapter_material.py b/threestudio/models/materials/sd_latent_adapter_material.py new file mode 100644 index 0000000..046cabb --- /dev/null +++ b/threestudio/models/materials/sd_latent_adapter_material.py @@ -0,0 +1,42 @@ +import random +from dataclasses import dataclass, field + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.models.materials.base import BaseMaterial +from threestudio.utils.typing import * + + +@threestudio.register("sd-latent-adapter-material") +class StableDiffusionLatentAdapterMaterial(BaseMaterial): + @dataclass + class Config(BaseMaterial.Config): + pass + + cfg: Config + + def configure(self) -> None: + adapter = nn.Parameter( + torch.as_tensor( + [ + # R G B + [0.298, 0.207, 0.208], # L1 + [0.187, 0.286, 0.173], # L2 + [-0.158, 0.189, 0.264], # L3 + [-0.184, -0.271, -0.473], # L4 + ] + ) + ) + self.register_parameter("adapter", adapter) + + def forward( + self, features: Float[Tensor, "B ... 4"], **kwargs + ) -> Float[Tensor, "B ... 3"]: + assert features.shape[-1] == 4 + color = features @ self.adapter + color = (color + 1) / 2 + color = color.clamp(0.0, 1.0) + return color diff --git a/threestudio/models/mesh.py b/threestudio/models/mesh.py new file mode 100644 index 0000000..232fa7c --- /dev/null +++ b/threestudio/models/mesh.py @@ -0,0 +1,309 @@ +from __future__ import annotations + +import numpy as np +import torch +import torch.nn.functional as F + +import threestudio +from threestudio.utils.ops import dot +from threestudio.utils.typing import * + + +class Mesh: + def __init__( + self, v_pos: Float[Tensor, "Nv 3"], t_pos_idx: Integer[Tensor, "Nf 3"], **kwargs + ) -> None: + self.v_pos: Float[Tensor, "Nv 3"] = v_pos + self.t_pos_idx: Integer[Tensor, "Nf 3"] = t_pos_idx + self._v_nrm: Optional[Float[Tensor, "Nv 3"]] = None + self._v_tng: Optional[Float[Tensor, "Nv 3"]] = None + self._v_tex: Optional[Float[Tensor, "Nt 3"]] = None + self._t_tex_idx: Optional[Float[Tensor, "Nf 3"]] = None + self._v_rgb: Optional[Float[Tensor, "Nv 3"]] = None + self._edges: Optional[Integer[Tensor, "Ne 2"]] = None + self.extras: Dict[str, Any] = {} + for k, v in kwargs.items(): + self.add_extra(k, v) + + def add_extra(self, k, v) -> None: + self.extras[k] = v + + def remove_outlier(self, outlier_n_faces_threshold: Union[int, float]) -> Mesh: + if self.requires_grad: + threestudio.debug("Mesh is differentiable, not removing outliers") + return self + + # use trimesh to first split the mesh into connected components + # then remove the components with less than n_face_threshold faces + import trimesh + + # construct a trimesh object + mesh = trimesh.Trimesh( + vertices=self.v_pos.detach().cpu().numpy(), + faces=self.t_pos_idx.detach().cpu().numpy(), + ) + + # split the mesh into connected components + components = mesh.split(only_watertight=False) + # log the number of faces in each component + threestudio.debug( + "Mesh has {} components, with faces: {}".format( + len(components), [c.faces.shape[0] for c in components] + ) + ) + + n_faces_threshold: int + if isinstance(outlier_n_faces_threshold, float): + # set the threshold to the number of faces in the largest component multiplied by outlier_n_faces_threshold + n_faces_threshold = int( + max([c.faces.shape[0] for c in components]) * outlier_n_faces_threshold + ) + else: + # set the threshold directly to outlier_n_faces_threshold + n_faces_threshold = outlier_n_faces_threshold + + # log the threshold + threestudio.debug( + "Removing components with less than {} faces".format(n_faces_threshold) + ) + + # remove the components with less than n_face_threshold faces + components = [c for c in components if c.faces.shape[0] >= n_faces_threshold] + + # log the number of faces in each component after removing outliers + threestudio.debug( + "Mesh has {} components after removing outliers, with faces: {}".format( + len(components), [c.faces.shape[0] for c in components] + ) + ) + # merge the components + mesh = trimesh.util.concatenate(components) + + # convert back to our mesh format + v_pos = torch.from_numpy(mesh.vertices).to(self.v_pos) + t_pos_idx = torch.from_numpy(mesh.faces).to(self.t_pos_idx) + + clean_mesh = Mesh(v_pos, t_pos_idx) + # keep the extras unchanged + + if len(self.extras) > 0: + clean_mesh.extras = self.extras + threestudio.debug( + f"The following extra attributes are inherited from the original mesh unchanged: {list(self.extras.keys())}" + ) + return clean_mesh + + @property + def requires_grad(self): + return self.v_pos.requires_grad + + @property + def v_nrm(self): + if self._v_nrm is None: + self._v_nrm = self._compute_vertex_normal() + return self._v_nrm + + @property + def v_tng(self): + if self._v_tng is None: + self._v_tng = self._compute_vertex_tangent() + return self._v_tng + + @property + def v_tex(self): + if self._v_tex is None: + self._v_tex, self._t_tex_idx = self._unwrap_uv() + return self._v_tex + + @property + def t_tex_idx(self): + if self._t_tex_idx is None: + self._v_tex, self._t_tex_idx = self._unwrap_uv() + return self._t_tex_idx + + @property + def v_rgb(self): + return self._v_rgb + + @property + def edges(self): + if self._edges is None: + self._edges = self._compute_edges() + return self._edges + + def _compute_vertex_normal(self): + i0 = self.t_pos_idx[:, 0] + i1 = self.t_pos_idx[:, 1] + i2 = self.t_pos_idx[:, 2] + + v0 = self.v_pos[i0, :] + v1 = self.v_pos[i1, :] + v2 = self.v_pos[i2, :] + + face_normals = torch.cross(v1 - v0, v2 - v0) + + # Splat face normals to vertices + v_nrm = torch.zeros_like(self.v_pos) + v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals) + v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals) + v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals) + + # Normalize, replace zero (degenerated) normals with some default value + v_nrm = torch.where( + dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm) + ) + v_nrm = F.normalize(v_nrm, dim=1) + + if torch.is_anomaly_enabled(): + assert torch.all(torch.isfinite(v_nrm)) + + return v_nrm + + def _compute_vertex_tangent(self): + vn_idx = [None] * 3 + pos = [None] * 3 + tex = [None] * 3 + for i in range(0, 3): + pos[i] = self.v_pos[self.t_pos_idx[:, i]] + tex[i] = self.v_tex[self.t_tex_idx[:, i]] + # t_nrm_idx is always the same as t_pos_idx + vn_idx[i] = self.t_pos_idx[:, i] + + tangents = torch.zeros_like(self.v_nrm) + tansum = torch.zeros_like(self.v_nrm) + + # Compute tangent space for each triangle + uve1 = tex[1] - tex[0] + uve2 = tex[2] - tex[0] + pe1 = pos[1] - pos[0] + pe2 = pos[2] - pos[0] + + nom = pe1 * uve2[..., 1:2] - pe2 * uve1[..., 1:2] + denom = uve1[..., 0:1] * uve2[..., 1:2] - uve1[..., 1:2] * uve2[..., 0:1] + + # Avoid division by zero for degenerated texture coordinates + tang = nom / torch.where( + denom > 0.0, torch.clamp(denom, min=1e-6), torch.clamp(denom, max=-1e-6) + ) + + # Update all 3 vertices + for i in range(0, 3): + idx = vn_idx[i][:, None].repeat(1, 3) + tangents.scatter_add_(0, idx, tang) # tangents[n_i] = tangents[n_i] + tang + tansum.scatter_add_( + 0, idx, torch.ones_like(tang) + ) # tansum[n_i] = tansum[n_i] + 1 + tangents = tangents / tansum + + # Normalize and make sure tangent is perpendicular to normal + tangents = F.normalize(tangents, dim=1) + tangents = F.normalize(tangents - dot(tangents, self.v_nrm) * self.v_nrm) + + if torch.is_anomaly_enabled(): + assert torch.all(torch.isfinite(tangents)) + + return tangents + + def _unwrap_uv( + self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {} + ): + threestudio.info("Using xatlas to perform UV unwrapping, may take a while ...") + + import xatlas + + atlas = xatlas.Atlas() + atlas.add_mesh( + self.v_pos.detach().cpu().numpy(), + self.t_pos_idx.cpu().numpy(), + ) + co = xatlas.ChartOptions() + po = xatlas.PackOptions() + for k, v in xatlas_chart_options.items(): + setattr(co, k, v) + for k, v in xatlas_pack_options.items(): + setattr(po, k, v) + atlas.generate(co, po) + vmapping, indices, uvs = atlas.get_mesh(0) + vmapping = ( + torch.from_numpy( + vmapping.astype(np.uint64, casting="same_kind").view(np.int64) + ) + .to(self.v_pos.device) + .long() + ) + uvs = torch.from_numpy(uvs).to(self.v_pos.device).float() + indices = ( + torch.from_numpy( + indices.astype(np.uint64, casting="same_kind").view(np.int64) + ) + .to(self.v_pos.device) + .long() + ) + return uvs, indices + + def unwrap_uv( + self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {} + ): + self._v_tex, self._t_tex_idx = self._unwrap_uv( + xatlas_chart_options, xatlas_pack_options + ) + + def set_vertex_color(self, v_rgb): + assert v_rgb.shape[0] == self.v_pos.shape[0] + self._v_rgb = v_rgb + + def _compute_edges(self): + # Compute edges + edges = torch.cat( + [ + self.t_pos_idx[:, [0, 1]], + self.t_pos_idx[:, [1, 2]], + self.t_pos_idx[:, [2, 0]], + ], + dim=0, + ) + edges = edges.sort()[0] + edges = torch.unique(edges, dim=0) + return edges + + def normal_consistency(self) -> Float[Tensor, ""]: + edge_nrm: Float[Tensor, "Ne 2 3"] = self.v_nrm[self.edges] + nc = ( + 1.0 - torch.cosine_similarity(edge_nrm[:, 0], edge_nrm[:, 1], dim=-1) + ).mean() + return nc + + def _laplacian_uniform(self): + # from stable-dreamfusion + # https://github.com/ashawkey/stable-dreamfusion/blob/8fb3613e9e4cd1ded1066b46e80ca801dfb9fd06/nerf/renderer.py#L224 + verts, faces = self.v_pos, self.t_pos_idx + + V = verts.shape[0] + F = faces.shape[0] + + # Neighbor indices + ii = faces[:, [1, 2, 0]].flatten() + jj = faces[:, [2, 0, 1]].flatten() + adj = torch.stack([torch.cat([ii, jj]), torch.cat([jj, ii])], dim=0).unique( + dim=1 + ) + adj_values = torch.ones(adj.shape[1]).to(verts) + + # Diagonal indices + diag_idx = adj[0] + + # Build the sparse matrix + idx = torch.cat((adj, torch.stack((diag_idx, diag_idx), dim=0)), dim=1) + values = torch.cat((-adj_values, adj_values)) + + # The coalesce operation sums the duplicate indices, resulting in the + # correct diagonal + return torch.sparse_coo_tensor(idx, values, (V, V)).coalesce() + + def laplacian(self) -> Float[Tensor, ""]: + with torch.no_grad(): + L = self._laplacian_uniform() + loss = L.mm(self.v_pos) + loss = loss.norm(dim=1) + loss = loss.mean() + return loss diff --git a/threestudio/models/networks.py b/threestudio/models/networks.py new file mode 100644 index 0000000..cfe986e --- /dev/null +++ b/threestudio/models/networks.py @@ -0,0 +1,411 @@ +import math + +import tinycudann as tcnn +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.utils.base import Updateable +from threestudio.utils.config import config_to_primitive +from threestudio.utils.misc import get_rank +from threestudio.utils.ops import get_activation +from threestudio.utils.typing import * + + +class ProgressiveBandFrequency(nn.Module, Updateable): + def __init__(self, in_channels: int, config: dict): + super().__init__() + self.N_freqs = config["n_frequencies"] + self.in_channels, self.n_input_dims = in_channels, in_channels + self.funcs = [torch.sin, torch.cos] + self.freq_bands = 2 ** torch.linspace(0, self.N_freqs - 1, self.N_freqs) + self.n_output_dims = self.in_channels * (len(self.funcs) * self.N_freqs) + self.n_masking_step = config.get("n_masking_step", 0) + self.update_step( + None, None + ) # mask should be updated at the beginning each step + + def forward(self, x): + out = [] + for freq, mask in zip(self.freq_bands, self.mask): + for func in self.funcs: + out += [func(freq * x) * mask] + return torch.cat(out, -1) + + def update_step(self, epoch, global_step, on_load_weights=False): + if self.n_masking_step <= 0 or global_step is None: + self.mask = torch.ones(self.N_freqs, dtype=torch.float32) + else: + self.mask = ( + 1.0 + - torch.cos( + math.pi + * ( + global_step / self.n_masking_step * self.N_freqs + - torch.arange(0, self.N_freqs) + ).clamp(0, 1) + ) + ) / 2.0 + threestudio.debug( + f"Update mask: {global_step}/{self.n_masking_step} {self.mask}" + ) + + +class TCNNEncoding(nn.Module): + def __init__(self, in_channels, config, dtype=torch.float32) -> None: + super().__init__() + self.n_input_dims = in_channels + with torch.cuda.device(get_rank()): + self.encoding = tcnn.Encoding(in_channels, config, dtype=dtype) + self.n_output_dims = self.encoding.n_output_dims + + def forward(self, x): + return self.encoding(x) + + +# 4D implicit decomposition of space and time (4D-fy) +class TCNNEncodingSpatialTime(nn.Module): + def __init__( + self, in_channels, config, dtype=torch.float32, init_time_zero=False + ) -> None: + super().__init__() + self.n_input_dims = in_channels + config["otype"] = "HashGrid" + self.num_frames = 1 # config["num_frames"] + self.static = config["static"] + self.cfg = config_to_primitive(config) + self.cfg_time = self.cfg + self.n_key_frames = config.get("n_key_frames", 1) + with torch.cuda.device(get_rank()): + self.encoding = tcnn.Encoding(self.n_input_dims, self.cfg, dtype=dtype) + self.encoding_time = tcnn.Encoding( + self.n_input_dims + 1, self.cfg_time, dtype=dtype + ) + self.n_output_dims = self.encoding.n_output_dims + self.frame_time = None + if self.static: + self.set_temp_param_grad(requires_grad=False) + self.use_key_frame = config.get("use_key_frame", False) + self.is_video = True + self.update_occ_grid = False + + def set_temp_param_grad(self, requires_grad=False): + self.set_param_grad(self.encoding_time, requires_grad=requires_grad) + + def set_param_grad(self, param_list, requires_grad=False): + if isinstance(param_list, nn.Parameter): + param_list.requires_grad = requires_grad + else: + for param in param_list.parameters(): + param.requires_grad = requires_grad + + def forward(self, x): + # TODO frame_time only supports batch_size == 1 cases + if self.update_occ_grid and not isinstance(self.frame_time, float): + frame_time = self.frame_time + else: + if (self.static or not self.training) and self.frame_time is None: + frame_time = torch.zeros( + (self.num_frames, 1), device=x.device, dtype=x.dtype + ).expand(x.shape[0], 1) + else: + if self.frame_time is None: + frame_time = 0.0 + else: + frame_time = self.frame_time + frame_time = ( + torch.ones((self.num_frames, 1), device=x.device, dtype=x.dtype) + * frame_time + ).expand(x.shape[0], 1) + frame_time = frame_time.view(-1, 1) + enc_space = self.encoding(x) + x_frame_time = torch.cat((x, frame_time), 1) + enc_space_time = self.encoding_time(x_frame_time) + enc = enc_space + enc_space_time + return enc + + +class ProgressiveBandHashGrid(nn.Module, Updateable): + def __init__(self, in_channels, config, dtype=torch.float32): + super().__init__() + self.n_input_dims = in_channels + encoding_config = config.copy() + encoding_config["otype"] = "Grid" + encoding_config["type"] = "Hash" + with torch.cuda.device(get_rank()): + self.encoding = tcnn.Encoding(in_channels, encoding_config, dtype=dtype) + self.n_output_dims = self.encoding.n_output_dims + self.n_level = config["n_levels"] + self.n_features_per_level = config["n_features_per_level"] + self.start_level, self.start_step, self.update_steps = ( + config["start_level"], + config["start_step"], + config["update_steps"], + ) + self.current_level = self.start_level + self.mask = torch.zeros( + self.n_level * self.n_features_per_level, + dtype=torch.float32, + device=get_rank(), + ) + + def forward(self, x): + enc = self.encoding(x) + enc = enc * self.mask + return enc + + def update_step(self, epoch, global_step, on_load_weights=False): + current_level = min( + self.start_level + + max(global_step - self.start_step, 0) // self.update_steps, + self.n_level, + ) + if current_level > self.current_level: + threestudio.debug(f"Update current level to {current_level}") + self.current_level = current_level + self.mask[: self.current_level * self.n_features_per_level] = 1.0 + + +class CompositeEncoding(nn.Module, Updateable): + def __init__(self, encoding, include_xyz=False, xyz_scale=2.0, xyz_offset=-1.0): + super(CompositeEncoding, self).__init__() + self.encoding = encoding + self.include_xyz, self.xyz_scale, self.xyz_offset = ( + include_xyz, + xyz_scale, + xyz_offset, + ) + self.n_output_dims = ( + int(self.include_xyz) * self.encoding.n_input_dims + + self.encoding.n_output_dims + ) + + def forward(self, x, *args): + return ( + self.encoding(x, *args) + if not self.include_xyz + else torch.cat( + [x * self.xyz_scale + self.xyz_offset, self.encoding(x, *args)], dim=-1 + ) + ) + + +def get_encoding(n_input_dims: int, config) -> nn.Module: + # input suppose to be range [0, 1] + encoding: nn.Module + if config.otype == "ProgressiveBandFrequency": + encoding = ProgressiveBandFrequency(n_input_dims, config_to_primitive(config)) + elif config.otype == "ProgressiveBandHashGrid": + encoding = ProgressiveBandHashGrid(n_input_dims, config_to_primitive(config)) + elif config.otype == "HashGridSpatialTime": + encoding = TCNNEncodingSpatialTime(n_input_dims, config) # 4D-fy encoding + else: + encoding = TCNNEncoding(n_input_dims, config_to_primitive(config)) + encoding = CompositeEncoding( + encoding, + include_xyz=config.get("include_xyz", False), + xyz_scale=2.0, + xyz_offset=-1.0, + ) # FIXME: hard coded + return encoding + + +class VanillaMLP(nn.Module): + def __init__(self, dim_in: int, dim_out: int, config: dict): + super().__init__() + self.n_neurons, self.n_hidden_layers = ( + config["n_neurons"], + config["n_hidden_layers"], + ) + layers = [ + self.make_linear(dim_in, self.n_neurons, is_first=True, is_last=False), + self.make_activation(), + ] + for i in range(self.n_hidden_layers - 1): + layers += [ + self.make_linear( + self.n_neurons, self.n_neurons, is_first=False, is_last=False + ), + self.make_activation(), + ] + layers += [ + self.make_linear(self.n_neurons, dim_out, is_first=False, is_last=True) + ] + self.layers = nn.Sequential(*layers) + self.output_activation = get_activation(config.get("output_activation", None)) + + def forward(self, x): + # disable autocast + # strange that the parameters will have empty gradients if autocast is enabled in AMP + with torch.cuda.amp.autocast(enabled=False): + x = self.layers(x) + x = self.output_activation(x) + return x + + def make_linear(self, dim_in, dim_out, is_first, is_last): + layer = nn.Linear(dim_in, dim_out, bias=False) + return layer + + def make_activation(self): + return nn.ReLU(inplace=True) + + +class SphereInitVanillaMLP(nn.Module): + def __init__(self, dim_in, dim_out, config): + super().__init__() + self.n_neurons, self.n_hidden_layers = ( + config["n_neurons"], + config["n_hidden_layers"], + ) + self.sphere_init, self.weight_norm = True, True + self.sphere_init_radius = config["sphere_init_radius"] + self.sphere_init_inside_out = config["inside_out"] + + self.layers = [ + self.make_linear(dim_in, self.n_neurons, is_first=True, is_last=False), + self.make_activation(), + ] + for i in range(self.n_hidden_layers - 1): + self.layers += [ + self.make_linear( + self.n_neurons, self.n_neurons, is_first=False, is_last=False + ), + self.make_activation(), + ] + self.layers += [ + self.make_linear(self.n_neurons, dim_out, is_first=False, is_last=True) + ] + self.layers = nn.Sequential(*self.layers) + self.output_activation = get_activation(config.get("output_activation", None)) + + def forward(self, x): + # disable autocast + # strange that the parameters will have empty gradients if autocast is enabled in AMP + with torch.cuda.amp.autocast(enabled=False): + x = self.layers(x) + x = self.output_activation(x) + return x + + def make_linear(self, dim_in, dim_out, is_first, is_last): + layer = nn.Linear(dim_in, dim_out, bias=True) + + if is_last: + if not self.sphere_init_inside_out: + torch.nn.init.constant_(layer.bias, -self.sphere_init_radius) + torch.nn.init.normal_( + layer.weight, + mean=math.sqrt(math.pi) / math.sqrt(dim_in), + std=0.0001, + ) + else: + torch.nn.init.constant_(layer.bias, self.sphere_init_radius) + torch.nn.init.normal_( + layer.weight, + mean=-math.sqrt(math.pi) / math.sqrt(dim_in), + std=0.0001, + ) + elif is_first: + torch.nn.init.constant_(layer.bias, 0.0) + torch.nn.init.constant_(layer.weight[:, 3:], 0.0) + torch.nn.init.normal_( + layer.weight[:, :3], 0.0, math.sqrt(2) / math.sqrt(dim_out) + ) + else: + torch.nn.init.constant_(layer.bias, 0.0) + torch.nn.init.normal_(layer.weight, 0.0, math.sqrt(2) / math.sqrt(dim_out)) + + if self.weight_norm: + layer = nn.utils.weight_norm(layer) + return layer + + def make_activation(self): + return nn.Softplus(beta=100) + + +class TCNNNetwork(nn.Module): + def __init__(self, dim_in: int, dim_out: int, config: dict) -> None: + super().__init__() + with torch.cuda.device(get_rank()): + self.network = tcnn.Network(dim_in, dim_out, config) + + def forward(self, x): + return self.network(x).float() # transform to float32 + + +def get_mlp(n_input_dims, n_output_dims, config) -> nn.Module: + network: nn.Module + if config.otype == "VanillaMLP": + network = VanillaMLP(n_input_dims, n_output_dims, config_to_primitive(config)) + elif config.otype == "SphereInitVanillaMLP": + network = SphereInitVanillaMLP( + n_input_dims, n_output_dims, config_to_primitive(config) + ) + else: + assert ( + config.get("sphere_init", False) is False + ), "sphere_init=True only supported by VanillaMLP" + network = TCNNNetwork(n_input_dims, n_output_dims, config_to_primitive(config)) + return network + + +class NetworkWithInputEncoding(nn.Module, Updateable): + def __init__(self, encoding, network): + super().__init__() + self.encoding, self.network = encoding, network + + def forward(self, x): + return self.network(self.encoding(x)) + + +class TCNNNetworkWithInputEncoding(nn.Module): + def __init__( + self, + n_input_dims: int, + n_output_dims: int, + encoding_config: dict, + network_config: dict, + ) -> None: + super().__init__() + with torch.cuda.device(get_rank()): + self.network_with_input_encoding = tcnn.NetworkWithInputEncoding( + n_input_dims=n_input_dims, + n_output_dims=n_output_dims, + encoding_config=encoding_config, + network_config=network_config, + ) + + def forward(self, x): + return self.network_with_input_encoding(x).float() # transform to float32 + + +def create_network_with_input_encoding( + n_input_dims: int, n_output_dims: int, encoding_config, network_config +) -> nn.Module: + # input suppose to be range [0, 1] + network_with_input_encoding: nn.Module + if encoding_config.otype in [ + "VanillaFrequency", + "ProgressiveBandHashGrid", + ] or network_config.otype in ["VanillaMLP", "SphereInitVanillaMLP"]: + encoding = get_encoding(n_input_dims, encoding_config) + network = get_mlp(encoding.n_output_dims, n_output_dims, network_config) + network_with_input_encoding = NetworkWithInputEncoding(encoding, network) + else: + network_with_input_encoding = TCNNNetworkWithInputEncoding( + n_input_dims=n_input_dims, + n_output_dims=n_output_dims, + encoding_config=config_to_primitive(encoding_config), + network_config=config_to_primitive(network_config), + ) + return network_with_input_encoding + + +class ToDTypeWrapper(nn.Module): + def __init__(self, module: nn.Module, dtype: torch.dtype): + super().__init__() + self.module = module + self.dtype = dtype + + def forward(self, x: Float[Tensor, "..."]) -> Float[Tensor, "..."]: + return self.module(x).to(self.dtype) diff --git a/threestudio/models/prompt_processors/__init__.py b/threestudio/models/prompt_processors/__init__.py new file mode 100644 index 0000000..eb45c70 --- /dev/null +++ b/threestudio/models/prompt_processors/__init__.py @@ -0,0 +1,6 @@ +from . import ( + base, + deepfloyd_prompt_processor, + dummy_prompt_processor, + stable_diffusion_prompt_processor, +) diff --git a/threestudio/models/prompt_processors/base.py b/threestudio/models/prompt_processors/base.py new file mode 100644 index 0000000..9a402da --- /dev/null +++ b/threestudio/models/prompt_processors/base.py @@ -0,0 +1,523 @@ +import json +import os +from dataclasses import dataclass, field + +import torch +import torch.multiprocessing as mp +import torch.nn as nn +import torch.nn.functional as F +from pytorch_lightning.utilities.rank_zero import rank_zero_only +from transformers import AutoTokenizer, BertForMaskedLM + +import threestudio +from threestudio.utils.base import BaseObject +from threestudio.utils.misc import barrier, cleanup, get_rank +from threestudio.utils.ops import shifted_cosine_decay, shifted_expotional_decay +from threestudio.utils.typing import * + + +def hash_prompt(model: str, prompt: str) -> str: + import hashlib + + identifier = f"{model}-{prompt}" + return hashlib.md5(identifier.encode()).hexdigest() + + +@dataclass +class DirectionConfig: + name: str + prompt: Callable[[str], str] + negative_prompt: Callable[[str], str] + condition: Callable[ + [Float[Tensor, "B"], Float[Tensor, "B"], Float[Tensor, "B"]], + Float[Tensor, "B"], + ] + + +@dataclass +class PromptProcessorOutput: + text_embeddings: Float[Tensor, "N Nf"] + uncond_text_embeddings: Float[Tensor, "N Nf"] + text_embeddings_vd: Float[Tensor, "Nv N Nf"] + uncond_text_embeddings_vd: Float[Tensor, "Nv N Nf"] + directions: List[DirectionConfig] + direction2idx: Dict[str, int] + use_perp_neg: bool + perp_neg_f_sb: Tuple[float, float, float] + perp_neg_f_fsb: Tuple[float, float, float] + perp_neg_f_fs: Tuple[float, float, float] + perp_neg_f_sf: Tuple[float, float, float] + prompt: str + prompts_vd: List[str] + + def get_text_embeddings( + self, + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + view_dependent_prompting: bool = True, + ) -> Float[Tensor, "BB N Nf"]: + batch_size = elevation.shape[0] + + if view_dependent_prompting: + # Get direction + direction_idx = torch.zeros_like(elevation, dtype=torch.long) + for d in self.directions: + direction_idx[ + d.condition(elevation, azimuth, camera_distances) + ] = self.direction2idx[d.name] + + # Get text embeddings + text_embeddings = self.text_embeddings_vd[direction_idx] # type: ignore + uncond_text_embeddings = self.uncond_text_embeddings_vd[direction_idx] # type: ignore + else: + text_embeddings = self.text_embeddings.expand(batch_size, -1, -1) # type: ignore + uncond_text_embeddings = self.uncond_text_embeddings.expand( # type: ignore + batch_size, -1, -1 + ) + + # IMPORTANT: we return (cond, uncond), which is in different order than other implementations! + return torch.cat([text_embeddings, uncond_text_embeddings], dim=0) + + def get_text_embeddings_perp_neg( + self, + elevation: Float[Tensor, "B"], + azimuth: Float[Tensor, "B"], + camera_distances: Float[Tensor, "B"], + view_dependent_prompting: bool = True, + ) -> Tuple[Float[Tensor, "BBBB N Nf"], Float[Tensor, "B 2"]]: + assert ( + view_dependent_prompting + ), "Perp-Neg only works with view-dependent prompting" + + batch_size = elevation.shape[0] + + direction_idx = torch.zeros_like(elevation, dtype=torch.long) + for d in self.directions: + direction_idx[ + d.condition(elevation, azimuth, camera_distances) + ] = self.direction2idx[d.name] + # 0 - side view + # 1 - front view + # 2 - back view + # 3 - overhead view + + pos_text_embeddings = [] + neg_text_embeddings = [] + neg_guidance_weights = [] + uncond_text_embeddings = [] + + side_emb = self.text_embeddings_vd[0] + front_emb = self.text_embeddings_vd[1] + back_emb = self.text_embeddings_vd[2] + overhead_emb = self.text_embeddings_vd[3] + + for idx, ele, azi, dis in zip( + direction_idx, elevation, azimuth, camera_distances + ): + azi = shift_azimuth_deg(azi) # to (-180, 180) + uncond_text_embeddings.append( + self.uncond_text_embeddings_vd[idx] + ) # should be "" + if idx.item() == 3: # overhead view + pos_text_embeddings.append(overhead_emb) # side view + # dummy + neg_text_embeddings += [ + self.uncond_text_embeddings_vd[idx], + self.uncond_text_embeddings_vd[idx], + ] + neg_guidance_weights += [0.0, 0.0] + else: # interpolating views + if torch.abs(azi) < 90: + # front-side interpolation + # 0 - complete side, 1 - complete front + r_inter = 1 - torch.abs(azi) / 90 + pos_text_embeddings.append( + r_inter * front_emb + (1 - r_inter) * side_emb + ) + neg_text_embeddings += [front_emb, side_emb] + neg_guidance_weights += [ + -shifted_expotional_decay(*self.perp_neg_f_fs, r_inter), + -shifted_expotional_decay(*self.perp_neg_f_sf, 1 - r_inter), + ] + else: + # side-back interpolation + # 0 - complete back, 1 - complete side + r_inter = 2.0 - torch.abs(azi) / 90 + pos_text_embeddings.append( + r_inter * side_emb + (1 - r_inter) * back_emb + ) + neg_text_embeddings += [side_emb, front_emb] + neg_guidance_weights += [ + -shifted_expotional_decay(*self.perp_neg_f_sb, r_inter), + -shifted_expotional_decay(*self.perp_neg_f_fsb, r_inter), + ] + + text_embeddings = torch.cat( + [ + torch.stack(pos_text_embeddings, dim=0), + torch.stack(uncond_text_embeddings, dim=0), + torch.stack(neg_text_embeddings, dim=0), + ], + dim=0, + ) + + return text_embeddings, torch.as_tensor( + neg_guidance_weights, device=elevation.device + ).reshape(batch_size, 2) + + +def shift_azimuth_deg(azimuth: Float[Tensor, "..."]) -> Float[Tensor, "..."]: + # shift azimuth angle (in degrees), to [-180, 180] + return (azimuth + 180) % 360 - 180 + + +class PromptProcessor(BaseObject): + @dataclass + class Config(BaseObject.Config): + prompt: str = "a hamburger" + + # manually assigned view-dependent prompts + prompt_front: Optional[str] = None + prompt_side: Optional[str] = None + prompt_back: Optional[str] = None + prompt_overhead: Optional[str] = None + + negative_prompt: str = "" + pretrained_model_name_or_path: str = "runwayml/stable-diffusion-v1-5" + overhead_threshold: float = 60.0 + front_threshold: float = 45.0 + back_threshold: float = 45.0 + view_dependent_prompt_front: bool = False + use_cache: bool = True + spawn: bool = True + + # perp neg + use_perp_neg: bool = False + # a*e(-b*r) + c + # a * e(-b) + c = 0 + perp_neg_f_sb: Tuple[float, float, float] = (1, 0.5, -0.606) + perp_neg_f_fsb: Tuple[float, float, float] = (1, 0.5, +0.967) + perp_neg_f_fs: Tuple[float, float, float] = ( + 4, + 0.5, + -2.426, + ) # f_fs(1) = 0, a, b > 0 + perp_neg_f_sf: Tuple[float, float, float] = (4, 0.5, -2.426) + + # prompt debiasing + use_prompt_debiasing: bool = False + pretrained_model_name_or_path_prompt_debiasing: str = "bert-base-uncased" + # index of words that can potentially be removed + prompt_debiasing_mask_ids: Optional[List[int]] = None + + cfg: Config + + @rank_zero_only + def configure_text_encoder(self) -> None: + raise NotImplementedError + + @rank_zero_only + def destroy_text_encoder(self) -> None: + raise NotImplementedError + + def configure(self) -> None: + self._cache_dir = ".threestudio_cache/text_embeddings" # FIXME: hard-coded path + + # view-dependent text embeddings + self.directions: List[DirectionConfig] + if self.cfg.view_dependent_prompt_front: + self.directions = [ + DirectionConfig( + "side", + lambda s: f"side view of {s}", + lambda s: s, + lambda ele, azi, dis: torch.ones_like(ele, dtype=torch.bool), + ), + DirectionConfig( + "front", + lambda s: f"front view of {s}", + lambda s: s, + lambda ele, azi, dis: ( + shift_azimuth_deg(azi) > -self.cfg.front_threshold + ) + & (shift_azimuth_deg(azi) < self.cfg.front_threshold), + ), + DirectionConfig( + "back", + lambda s: f"backside view of {s}", + lambda s: s, + lambda ele, azi, dis: ( + shift_azimuth_deg(azi) > 180 - self.cfg.back_threshold + ) + | (shift_azimuth_deg(azi) < -180 + self.cfg.back_threshold), + ), + DirectionConfig( + "overhead", + lambda s: f"overhead view of {s}", + lambda s: s, + lambda ele, azi, dis: ele > self.cfg.overhead_threshold, + ), + ] + else: + self.directions = [ + DirectionConfig( + "side", + lambda s: f"{s}, side view", + lambda s: s, + lambda ele, azi, dis: torch.ones_like(ele, dtype=torch.bool), + ), + DirectionConfig( + "front", + lambda s: f"{s}, front view", + lambda s: s, + lambda ele, azi, dis: ( + shift_azimuth_deg(azi) > -self.cfg.front_threshold + ) + & (shift_azimuth_deg(azi) < self.cfg.front_threshold), + ), + DirectionConfig( + "back", + lambda s: f"{s}, back view", + lambda s: s, + lambda ele, azi, dis: ( + shift_azimuth_deg(azi) > 180 - self.cfg.back_threshold + ) + | (shift_azimuth_deg(azi) < -180 + self.cfg.back_threshold), + ), + DirectionConfig( + "overhead", + lambda s: f"{s}, overhead view", + lambda s: s, + lambda ele, azi, dis: ele > self.cfg.overhead_threshold, + ), + ] + + self.direction2idx = {d.name: i for i, d in enumerate(self.directions)} + + if os.path.exists("load/prompt_library.json"): + with open(os.path.join("load/prompt_library.json"), "r") as f: + self.prompt_library = json.load(f) + else: + self.prompt_library = {} + # use provided prompt or find prompt in library + self.prompt = self.preprocess_prompt(self.cfg.prompt) + # use provided negative prompt + self.negative_prompt = self.cfg.negative_prompt + + threestudio.info( + f"Using prompt [{self.prompt}] and negative prompt [{self.negative_prompt}]" + ) + + # view-dependent prompting + if self.cfg.use_prompt_debiasing: + assert ( + self.cfg.prompt_side is None + and self.cfg.prompt_back is None + and self.cfg.prompt_overhead is None + ), "Do not manually assign prompt_side, prompt_back or prompt_overhead when using prompt debiasing" + prompts = self.get_debiased_prompt(self.prompt) + self.prompts_vd = [ + d.prompt(prompt) for d, prompt in zip(self.directions, prompts) + ] + else: + self.prompts_vd = [ + self.cfg.get(f"prompt_{d.name}", None) or d.prompt(self.prompt) # type: ignore + for d in self.directions + ] + + prompts_vd_display = " ".join( + [ + f"[{d.name}]:[{prompt}]" + for prompt, d in zip(self.prompts_vd, self.directions) + ] + ) + threestudio.info(f"Using view-dependent prompts {prompts_vd_display}") + + self.negative_prompts_vd = [ + d.negative_prompt(self.negative_prompt) for d in self.directions + ] + + self.prepare_text_embeddings() + self.load_text_embeddings() + + @staticmethod + def spawn_func(pretrained_model_name_or_path, prompts, cache_dir): + raise NotImplementedError + + @rank_zero_only + def prepare_text_embeddings(self): + os.makedirs(self._cache_dir, exist_ok=True) + + all_prompts = ( + [self.prompt] + + [self.negative_prompt] + + self.prompts_vd + + self.negative_prompts_vd + ) + prompts_to_process = [] + for prompt in all_prompts: + if self.cfg.use_cache: + # some text embeddings are already in cache + # do not process them + cache_path = os.path.join( + self._cache_dir, + f"{hash_prompt(self.cfg.pretrained_model_name_or_path, prompt)}.pt", + ) + if os.path.exists(cache_path): + threestudio.debug( + f"Text embeddings for model {self.cfg.pretrained_model_name_or_path} and prompt [{prompt}] are already in cache, skip processing." + ) + continue + prompts_to_process.append(prompt) + + if len(prompts_to_process) > 0: + if self.cfg.spawn: + ctx = mp.get_context("spawn") + subprocess = ctx.Process( + target=self.spawn_func, + args=( + self.cfg.pretrained_model_name_or_path, + prompts_to_process, + self._cache_dir, + ), + ) + subprocess.start() + subprocess.join() + assert subprocess.exitcode == 0, "prompt embedding process failed!" + else: + self.spawn_func( + self.cfg.pretrained_model_name_or_path, + prompts_to_process, + self._cache_dir, + ) + cleanup() + + def load_text_embeddings(self): + # synchronize, to ensure the text embeddings have been computed and saved to cache + barrier() + self.text_embeddings = self.load_from_cache(self.prompt)[None, ...] + self.uncond_text_embeddings = self.load_from_cache(self.negative_prompt)[ + None, ... + ] + self.text_embeddings_vd = torch.stack( + [self.load_from_cache(prompt) for prompt in self.prompts_vd], dim=0 + ) + self.uncond_text_embeddings_vd = torch.stack( + [self.load_from_cache(prompt) for prompt in self.negative_prompts_vd], dim=0 + ) + threestudio.debug(f"Loaded text embeddings.") + + def load_from_cache(self, prompt): + cache_path = os.path.join( + self._cache_dir, + f"{hash_prompt(self.cfg.pretrained_model_name_or_path, prompt)}.pt", + ) + if not os.path.exists(cache_path): + raise FileNotFoundError( + f"Text embedding file {cache_path} for model {self.cfg.pretrained_model_name_or_path} and prompt [{prompt}] not found." + ) + return torch.load(cache_path, map_location=self.device) + + def preprocess_prompt(self, prompt: str) -> str: + if prompt.startswith("lib:"): + # find matches in the library + candidate = None + keywords = prompt[4:].lower().split("_") + for prompt in self.prompt_library["dreamfusion"]: + if all([k in prompt.lower() for k in keywords]): + if candidate is not None: + raise ValueError( + f"Multiple prompts matched with keywords {keywords} in library" + ) + candidate = prompt + if candidate is None: + raise ValueError( + f"Cannot find prompt with keywords {keywords} in library" + ) + threestudio.info("Find matched prompt in library: " + candidate) + return candidate + else: + return prompt + + def get_text_embeddings( + self, prompt: Union[str, List[str]], negative_prompt: Union[str, List[str]] + ) -> Tuple[Float[Tensor, "B ..."], Float[Tensor, "B ..."]]: + raise NotImplementedError + + def get_debiased_prompt(self, prompt: str) -> List[str]: + os.environ["TOKENIZERS_PARALLELISM"] = "false" + + tokenizer = AutoTokenizer.from_pretrained( + self.cfg.pretrained_model_name_or_path_prompt_debiasing + ) + model = BertForMaskedLM.from_pretrained( + self.cfg.pretrained_model_name_or_path_prompt_debiasing + ) + + views = [d.name for d in self.directions] + view_ids = tokenizer(" ".join(views), return_tensors="pt").input_ids[0] + view_ids = view_ids[1:5] + + def modulate(prompt): + prompt_vd = f"This image is depicting a [MASK] view of {prompt}" + tokens = tokenizer( + prompt_vd, + padding="max_length", + truncation=True, + add_special_tokens=True, + return_tensors="pt", + ) + mask_idx = torch.where(tokens.input_ids == tokenizer.mask_token_id)[1] + + logits = model(**tokens).logits + logits = F.softmax(logits[0, mask_idx], dim=-1) + logits = logits[0, view_ids] + probes = logits / logits.sum() + return probes + + prompts = [prompt.split(" ") for _ in range(4)] + full_probe = modulate(prompt) + n_words = len(prompt.split(" ")) + prompt_debiasing_mask_ids = ( + self.cfg.prompt_debiasing_mask_ids + if self.cfg.prompt_debiasing_mask_ids is not None + else list(range(n_words)) + ) + words_to_debias = [prompt.split(" ")[idx] for idx in prompt_debiasing_mask_ids] + threestudio.info(f"Words that can potentially be removed: {words_to_debias}") + for idx in prompt_debiasing_mask_ids: + words = prompt.split(" ") + prompt_ = " ".join(words[:idx] + words[(idx + 1) :]) + part_probe = modulate(prompt_) + + pmi = full_probe / torch.lerp(part_probe, full_probe, 0.5) + for i in range(pmi.shape[0]): + if pmi[i].item() < 0.95: + prompts[i][idx] = "" + + debiased_prompts = [" ".join([word for word in p if word]) for p in prompts] + for d, debiased_prompt in zip(views, debiased_prompts): + threestudio.info(f"Debiased prompt of the {d} view is [{debiased_prompt}]") + + del tokenizer, model + cleanup() + + return debiased_prompts + + def __call__(self) -> PromptProcessorOutput: + return PromptProcessorOutput( + text_embeddings=self.text_embeddings, + uncond_text_embeddings=self.uncond_text_embeddings, + prompt=self.prompt, + text_embeddings_vd=self.text_embeddings_vd, + uncond_text_embeddings_vd=self.uncond_text_embeddings_vd, + prompts_vd=self.prompts_vd, + directions=self.directions, + direction2idx=self.direction2idx, + use_perp_neg=self.cfg.use_perp_neg, + perp_neg_f_sb=self.cfg.perp_neg_f_sb, + perp_neg_f_fsb=self.cfg.perp_neg_f_fsb, + perp_neg_f_fs=self.cfg.perp_neg_f_fs, + perp_neg_f_sf=self.cfg.perp_neg_f_sf, + ) diff --git a/threestudio/models/prompt_processors/deepfloyd_prompt_processor.py b/threestudio/models/prompt_processors/deepfloyd_prompt_processor.py new file mode 100644 index 0000000..6dd6eef --- /dev/null +++ b/threestudio/models/prompt_processors/deepfloyd_prompt_processor.py @@ -0,0 +1,95 @@ +import json +import os +from dataclasses import dataclass + +import torch +import torch.nn as nn +from diffusers import IFPipeline +from transformers import T5EncoderModel, T5Tokenizer + +import threestudio +from threestudio.models.prompt_processors.base import PromptProcessor, hash_prompt +from threestudio.utils.misc import cleanup +from threestudio.utils.typing import * + + +@threestudio.register("deep-floyd-prompt-processor") +class DeepFloydPromptProcessor(PromptProcessor): + @dataclass + class Config(PromptProcessor.Config): + pretrained_model_name_or_path: str = "DeepFloyd/IF-I-XL-v1.0" + + cfg: Config + + ### these functions are unused, kept for debugging ### + def configure_text_encoder(self) -> None: + os.environ["TOKENIZERS_PARALLELISM"] = "false" + self.text_encoder = T5EncoderModel.from_pretrained( + self.cfg.pretrained_model_name_or_path, + subfolder="text_encoder", + load_in_8bit=True, + variant="8bit", + device_map="auto", + ) # FIXME: behavior of auto device map in multi-GPU training + self.pipe = IFPipeline.from_pretrained( + self.cfg.pretrained_model_name_or_path, + text_encoder=self.text_encoder, # pass the previously instantiated 8bit text encoder + unet=None, + ) + + def destroy_text_encoder(self) -> None: + del self.text_encoder + del self.pipe + cleanup() + + def get_text_embeddings( + self, prompt: Union[str, List[str]], negative_prompt: Union[str, List[str]] + ) -> Tuple[Float[Tensor, "B 77 4096"], Float[Tensor, "B 77 4096"]]: + text_embeddings, uncond_text_embeddings = self.pipe.encode_prompt( + prompt=prompt, negative_prompt=negative_prompt, device=self.device + ) + return text_embeddings, uncond_text_embeddings + + ### + + @staticmethod + def spawn_func(pretrained_model_name_or_path, prompts, cache_dir): + max_length = 77 + tokenizer = T5Tokenizer.from_pretrained( + pretrained_model_name_or_path, subfolder="tokenizer" + ) + text_encoder = T5EncoderModel.from_pretrained( + pretrained_model_name_or_path, + subfolder="text_encoder", + torch_dtype=torch.float16, # suppress warning + load_in_8bit=True, + variant="8bit", + device_map="auto", + ) + with torch.no_grad(): + text_inputs = tokenizer( + prompts, + padding="max_length", + max_length=max_length, + truncation=True, + add_special_tokens=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + attention_mask = text_inputs.attention_mask + text_embeddings = text_encoder( + text_input_ids.to(text_encoder.device), + attention_mask=attention_mask.to(text_encoder.device), + ) + text_embeddings = text_embeddings[0] + + for prompt, embedding in zip(prompts, text_embeddings): + torch.save( + embedding, + os.path.join( + cache_dir, + f"{hash_prompt(pretrained_model_name_or_path, prompt)}.pt", + ), + ) + + del text_encoder diff --git a/threestudio/models/prompt_processors/dummy_prompt_processor.py b/threestudio/models/prompt_processors/dummy_prompt_processor.py new file mode 100644 index 0000000..cfbc6c4 --- /dev/null +++ b/threestudio/models/prompt_processors/dummy_prompt_processor.py @@ -0,0 +1,18 @@ +import json +import os +from dataclasses import dataclass + +import threestudio +from threestudio.models.prompt_processors.base import PromptProcessor, hash_prompt +from threestudio.utils.misc import cleanup +from threestudio.utils.typing import * + + +@threestudio.register("dummy-prompt-processor") +class DummyPromptProcessor(PromptProcessor): + @dataclass + class Config(PromptProcessor.Config): + pretrained_model_name_or_path: str = "" + prompt: str = "" + + cfg: Config diff --git a/threestudio/models/prompt_processors/stable_diffusion_prompt_processor.py b/threestudio/models/prompt_processors/stable_diffusion_prompt_processor.py new file mode 100644 index 0000000..91b9eeb --- /dev/null +++ b/threestudio/models/prompt_processors/stable_diffusion_prompt_processor.py @@ -0,0 +1,102 @@ +import json +import os +from dataclasses import dataclass + +import torch +import torch.nn as nn +from transformers import AutoTokenizer, CLIPTextModel + +import threestudio +from threestudio.models.prompt_processors.base import PromptProcessor, hash_prompt +from threestudio.utils.misc import cleanup +from threestudio.utils.typing import * + + +@threestudio.register("stable-diffusion-prompt-processor") +class StableDiffusionPromptProcessor(PromptProcessor): + @dataclass + class Config(PromptProcessor.Config): + pass + + cfg: Config + + ### these functions are unused, kept for debugging ### + def configure_text_encoder(self) -> None: + self.tokenizer = AutoTokenizer.from_pretrained( + self.cfg.pretrained_model_name_or_path, subfolder="tokenizer" + ) + os.environ["TOKENIZERS_PARALLELISM"] = "false" + self.text_encoder = CLIPTextModel.from_pretrained( + self.cfg.pretrained_model_name_or_path, subfolder="text_encoder" + ).to(self.device) + + for p in self.text_encoder.parameters(): + p.requires_grad_(False) + + def destroy_text_encoder(self) -> None: + del self.tokenizer + del self.text_encoder + cleanup() + + def get_text_embeddings( + self, prompt: Union[str, List[str]], negative_prompt: Union[str, List[str]] + ) -> Tuple[Float[Tensor, "B 77 768"], Float[Tensor, "B 77 768"]]: + if isinstance(prompt, str): + prompt = [prompt] + if isinstance(negative_prompt, str): + negative_prompt = [negative_prompt] + # Tokenize text and get embeddings + tokens = self.tokenizer( + prompt, + padding="max_length", + max_length=self.tokenizer.model_max_length, + return_tensors="pt", + ) + uncond_tokens = self.tokenizer( + negative_prompt, + padding="max_length", + max_length=self.tokenizer.model_max_length, + return_tensors="pt", + ) + + with torch.no_grad(): + text_embeddings = self.text_encoder(tokens.input_ids.to(self.device))[0] + uncond_text_embeddings = self.text_encoder( + uncond_tokens.input_ids.to(self.device) + )[0] + + return text_embeddings, uncond_text_embeddings + + ### + + @staticmethod + def spawn_func(pretrained_model_name_or_path, prompts, cache_dir): + os.environ["TOKENIZERS_PARALLELISM"] = "false" + tokenizer = AutoTokenizer.from_pretrained( + pretrained_model_name_or_path, subfolder="tokenizer" + ) + text_encoder = CLIPTextModel.from_pretrained( + pretrained_model_name_or_path, + subfolder="text_encoder", + device_map="auto", + ) + + with torch.no_grad(): + tokens = tokenizer( + prompts, + padding="max_length", + max_length=tokenizer.model_max_length, + return_tensors="pt", + ) + text_embeddings = text_encoder(tokens.input_ids.to(text_encoder.device))[0] + + for prompt, embedding in zip(prompts, text_embeddings): + torch.save( + embedding, + os.path.join( + cache_dir, + f"{hash_prompt(pretrained_model_name_or_path, prompt)}.pt", + ), + ) + + del text_encoder diff --git a/threestudio/models/renderers/__init__.py b/threestudio/models/renderers/__init__.py new file mode 100644 index 0000000..d33e0fc --- /dev/null +++ b/threestudio/models/renderers/__init__.py @@ -0,0 +1,9 @@ +from . import ( + base, + deferred_volume_renderer, + gan_volume_renderer, + nerf_volume_renderer, + neus_volume_renderer, + nvdiff_rasterizer, + patch_renderer, +) diff --git a/threestudio/models/renderers/base.py b/threestudio/models/renderers/base.py new file mode 100644 index 0000000..fa893c8 --- /dev/null +++ b/threestudio/models/renderers/base.py @@ -0,0 +1,80 @@ +from dataclasses import dataclass + +import nerfacc +import torch +import torch.nn.functional as F + +import threestudio +from threestudio.models.background.base import BaseBackground +from threestudio.models.geometry.base import BaseImplicitGeometry +from threestudio.models.materials.base import BaseMaterial +from threestudio.utils.base import BaseModule +from threestudio.utils.typing import * + + +class Renderer(BaseModule): + @dataclass + class Config(BaseModule.Config): + radius: float = 1.0 + + cfg: Config + + def configure( + self, + geometry: BaseImplicitGeometry, + material: BaseMaterial, + background: BaseBackground, + ) -> None: + # keep references to submodules using namedtuple, avoid being registered as modules + @dataclass + class SubModules: + geometry: BaseImplicitGeometry + material: BaseMaterial + background: BaseBackground + + self.sub_modules = SubModules(geometry, material, background) + + # set up bounding box + self.bbox: Float[Tensor, "2 3"] + self.register_buffer( + "bbox", + torch.as_tensor( + [ + [-self.cfg.radius, -self.cfg.radius, -self.cfg.radius], + [self.cfg.radius, self.cfg.radius, self.cfg.radius], + ], + dtype=torch.float32, + ), + ) + + def forward(self, *args, **kwargs) -> Dict[str, Any]: + raise NotImplementedError + + @property + def geometry(self) -> BaseImplicitGeometry: + return self.sub_modules.geometry + + @property + def material(self) -> BaseMaterial: + return self.sub_modules.material + + @property + def background(self) -> BaseBackground: + return self.sub_modules.background + + def set_geometry(self, geometry: BaseImplicitGeometry) -> None: + self.sub_modules.geometry = geometry + + def set_material(self, material: BaseMaterial) -> None: + self.sub_modules.material = material + + def set_background(self, background: BaseBackground) -> None: + self.sub_modules.background = background + + +class VolumeRenderer(Renderer): + pass + + +class Rasterizer(Renderer): + pass diff --git a/threestudio/models/renderers/deferred_volume_renderer.py b/threestudio/models/renderers/deferred_volume_renderer.py new file mode 100644 index 0000000..4c8f2ac --- /dev/null +++ b/threestudio/models/renderers/deferred_volume_renderer.py @@ -0,0 +1,11 @@ +from dataclasses import dataclass + +import torch +import torch.nn.functional as F + +import threestudio +from threestudio.models.renderers.base import VolumeRenderer + + +class DeferredVolumeRenderer(VolumeRenderer): + pass diff --git a/threestudio/models/renderers/gan_volume_renderer.py b/threestudio/models/renderers/gan_volume_renderer.py new file mode 100644 index 0000000..fed55a5 --- /dev/null +++ b/threestudio/models/renderers/gan_volume_renderer.py @@ -0,0 +1,159 @@ +from dataclasses import dataclass + +import torch +import torch.nn.functional as F + +import threestudio +from threestudio.models.background.base import BaseBackground +from threestudio.models.geometry.base import BaseImplicitGeometry +from threestudio.models.materials.base import BaseMaterial +from threestudio.models.renderers.base import VolumeRenderer +from threestudio.utils.GAN.discriminator import NLayerDiscriminator, weights_init +from threestudio.utils.GAN.distribution import DiagonalGaussianDistribution +from threestudio.utils.GAN.mobilenet import MobileNetV3 as GlobalEncoder +from threestudio.utils.GAN.vae import Decoder as Generator +from threestudio.utils.GAN.vae import Encoder as LocalEncoder +from threestudio.utils.typing import * + + +@threestudio.register("gan-volume-renderer") +class GANVolumeRenderer(VolumeRenderer): + @dataclass + class Config(VolumeRenderer.Config): + base_renderer_type: str = "" + base_renderer: Optional[VolumeRenderer.Config] = None + + cfg: Config + + def configure( + self, + geometry: BaseImplicitGeometry, + material: BaseMaterial, + background: BaseBackground, + ) -> None: + self.base_renderer = threestudio.find(self.cfg.base_renderer_type)( + self.cfg.base_renderer, + geometry=geometry, + material=material, + background=background, + ) + self.ch_mult = [1, 2, 4] + self.generator = Generator( + ch=64, + out_ch=3, + ch_mult=self.ch_mult, + num_res_blocks=1, + attn_resolutions=[], + dropout=0.0, + resamp_with_conv=True, + in_channels=7, + resolution=512, + z_channels=4, + ) + self.local_encoder = LocalEncoder( + ch=32, + out_ch=3, + ch_mult=self.ch_mult, + num_res_blocks=1, + attn_resolutions=[], + dropout=0.0, + resamp_with_conv=True, + in_channels=3, + resolution=512, + z_channels=4, + ) + self.global_encoder = GlobalEncoder(n_class=64) + self.discriminator = NLayerDiscriminator( + input_nc=3, n_layers=3, use_actnorm=False, ndf=64 + ).apply(weights_init) + + def forward( + self, + rays_o: Float[Tensor, "B H W 3"], + rays_d: Float[Tensor, "B H W 3"], + light_positions: Float[Tensor, "B 3"], + bg_color: Optional[Tensor] = None, + gt_rgb: Float[Tensor, "B H W 3"] = None, + multi_level_guidance: Bool = False, + **kwargs + ) -> Dict[str, Float[Tensor, "..."]]: + B, H, W, _ = rays_o.shape + if gt_rgb is not None and multi_level_guidance: + generator_level = torch.randint(0, 3, (1,)).item() + interval_x = torch.randint(0, 8, (1,)).item() + interval_y = torch.randint(0, 8, (1,)).item() + int_rays_o = rays_o[:, interval_y::8, interval_x::8] + int_rays_d = rays_d[:, interval_y::8, interval_x::8] + out = self.base_renderer( + int_rays_o, int_rays_d, light_positions, bg_color, **kwargs + ) + comp_int_rgb = out["comp_rgb"][..., :3] + comp_gt_rgb = gt_rgb[:, interval_y::8, interval_x::8] + else: + generator_level = 0 + scale_ratio = 2 ** (len(self.ch_mult) - 1) + rays_o = torch.nn.functional.interpolate( + rays_o.permute(0, 3, 1, 2), + (H // scale_ratio, W // scale_ratio), + mode="bilinear", + ).permute(0, 2, 3, 1) + rays_d = torch.nn.functional.interpolate( + rays_d.permute(0, 3, 1, 2), + (H // scale_ratio, W // scale_ratio), + mode="bilinear", + ).permute(0, 2, 3, 1) + + out = self.base_renderer(rays_o, rays_d, light_positions, bg_color, **kwargs) + comp_rgb = out["comp_rgb"][..., :3] + latent = out["comp_rgb"][..., 3:] + out["comp_lr_rgb"] = comp_rgb.clone() + + posterior = DiagonalGaussianDistribution(latent.permute(0, 3, 1, 2)) + if multi_level_guidance: + z_map = posterior.sample() + else: + z_map = posterior.mode() + lr_rgb = comp_rgb.permute(0, 3, 1, 2) + + if generator_level == 0: + g_code_rgb = self.global_encoder(F.interpolate(lr_rgb, (224, 224))) + comp_gan_rgb = self.generator(torch.cat([lr_rgb, z_map], dim=1), g_code_rgb) + elif generator_level == 1: + g_code_rgb = self.global_encoder( + F.interpolate(gt_rgb.permute(0, 3, 1, 2), (224, 224)) + ) + comp_gan_rgb = self.generator(torch.cat([lr_rgb, z_map], dim=1), g_code_rgb) + elif generator_level == 2: + g_code_rgb = self.global_encoder( + F.interpolate(gt_rgb.permute(0, 3, 1, 2), (224, 224)) + ) + l_code_rgb = self.local_encoder(gt_rgb.permute(0, 3, 1, 2)) + posterior = DiagonalGaussianDistribution(l_code_rgb) + z_map = posterior.sample() + comp_gan_rgb = self.generator(torch.cat([lr_rgb, z_map], dim=1), g_code_rgb) + + comp_rgb = F.interpolate(comp_rgb.permute(0, 3, 1, 2), (H, W), mode="bilinear") + comp_gan_rgb = F.interpolate(comp_gan_rgb, (H, W), mode="bilinear") + out.update( + { + "posterior": posterior, + "comp_gan_rgb": comp_gan_rgb.permute(0, 2, 3, 1), + "comp_rgb": comp_rgb.permute(0, 2, 3, 1), + "generator_level": generator_level, + } + ) + + if gt_rgb is not None and multi_level_guidance: + out.update({"comp_int_rgb": comp_int_rgb, "comp_gt_rgb": comp_gt_rgb}) + return out + + def update_step( + self, epoch: int, global_step: int, on_load_weights: bool = False + ) -> None: + self.base_renderer.update_step(epoch, global_step, on_load_weights) + + def train(self, mode=True): + return self.base_renderer.train(mode) + + def eval(self): + return self.base_renderer.eval() diff --git a/threestudio/models/renderers/nerf_volume_renderer.py b/threestudio/models/renderers/nerf_volume_renderer.py new file mode 100644 index 0000000..a36769c --- /dev/null +++ b/threestudio/models/renderers/nerf_volume_renderer.py @@ -0,0 +1,470 @@ +from dataclasses import dataclass, field +from functools import partial + +import nerfacc +import torch +import torch.nn.functional as F + +import threestudio +from threestudio.models.background.base import BaseBackground +from threestudio.models.estimators import ImportanceEstimator +from threestudio.models.geometry.base import BaseImplicitGeometry +from threestudio.models.materials.base import BaseMaterial +from threestudio.models.networks import create_network_with_input_encoding +from threestudio.models.renderers.base import VolumeRenderer +from threestudio.systems.utils import parse_optimizer, parse_scheduler_to_instance +from threestudio.utils.ops import chunk_batch, get_activation, validate_empty_rays +from threestudio.utils.typing import * + + +@threestudio.register("nerf-volume-renderer") +class NeRFVolumeRenderer(VolumeRenderer): + @dataclass + class Config(VolumeRenderer.Config): + num_samples_per_ray: int = 512 + eval_chunk_size: int = 160000 + randomized: bool = True + + near_plane: float = 0.0 + far_plane: float = 1e10 + + return_comp_normal: bool = False + return_normal_perturb: bool = False + + # in ["occgrid", "proposal", "importance"] + estimator: str = "occgrid" + + # for occgrid + grid_prune: bool = True + prune_alpha_threshold: bool = True + + # for proposal + proposal_network_config: Optional[dict] = None + prop_optimizer_config: Optional[dict] = None + prop_scheduler_config: Optional[dict] = None + num_samples_per_ray_proposal: int = 64 + + # for importance + num_samples_per_ray_importance: int = 64 + + cfg: Config + + def configure( + self, + geometry: BaseImplicitGeometry, + material: BaseMaterial, + background: BaseBackground, + ) -> None: + super().configure(geometry, material, background) + if self.cfg.estimator == "occgrid": + self.estimator = nerfacc.OccGridEstimator( + roi_aabb=self.bbox.view(-1), resolution=32, levels=1 + ) + if not self.cfg.grid_prune: + self.estimator.occs.fill_(True) + self.estimator.binaries.fill_(True) + self.render_step_size = ( + 1.732 * 2 * self.cfg.radius / self.cfg.num_samples_per_ray + ) + self.randomized = self.cfg.randomized + elif self.cfg.estimator == "importance": + self.estimator = ImportanceEstimator() + elif self.cfg.estimator == "proposal": + self.prop_net = create_network_with_input_encoding( + **self.cfg.proposal_network_config + ) + self.prop_optim = parse_optimizer( + self.cfg.prop_optimizer_config, self.prop_net + ) + self.prop_scheduler = ( + parse_scheduler_to_instance( + self.cfg.prop_scheduler_config, self.prop_optim + ) + if self.cfg.prop_scheduler_config is not None + else None + ) + self.estimator = nerfacc.PropNetEstimator( + self.prop_optim, self.prop_scheduler + ) + + def get_proposal_requires_grad_fn( + target: float = 5.0, num_steps: int = 1000 + ): + schedule = lambda s: min(s / num_steps, 1.0) * target + + steps_since_last_grad = 0 + + def proposal_requires_grad_fn(step: int) -> bool: + nonlocal steps_since_last_grad + target_steps_since_last_grad = schedule(step) + requires_grad = steps_since_last_grad > target_steps_since_last_grad + if requires_grad: + steps_since_last_grad = 0 + steps_since_last_grad += 1 + return requires_grad + + return proposal_requires_grad_fn + + self.proposal_requires_grad_fn = get_proposal_requires_grad_fn() + self.randomized = self.cfg.randomized + else: + raise NotImplementedError( + "Unknown estimator, should be one of ['occgrid', 'proposal', 'importance']." + ) + + # for proposal + self.vars_in_forward = {} + + def forward( + self, + rays_o: Float[Tensor, "B H W 3"], + rays_d: Float[Tensor, "B H W 3"], + light_positions: Float[Tensor, "B 3"], + bg_color: Optional[Tensor] = None, + **kwargs + ) -> Dict[str, Float[Tensor, "..."]]: + batch_size, height, width = rays_o.shape[:3] + rays_o_flatten: Float[Tensor, "Nr 3"] = rays_o.reshape(-1, 3) + rays_d_flatten: Float[Tensor, "Nr 3"] = rays_d.reshape(-1, 3) + light_positions_flatten: Float[Tensor, "Nr 3"] = ( + light_positions.reshape(-1, 1, 1, 3) + .expand(-1, height, width, -1) + .reshape(-1, 3) + ) + n_rays = rays_o_flatten.shape[0] + + if self.cfg.estimator == "occgrid": + if not self.cfg.grid_prune: + with torch.no_grad(): + ray_indices, t_starts_, t_ends_ = self.estimator.sampling( + rays_o_flatten, + rays_d_flatten, + sigma_fn=None, + near_plane=self.cfg.near_plane, + far_plane=self.cfg.far_plane, + render_step_size=self.render_step_size, + alpha_thre=0.0, + stratified=self.randomized, + cone_angle=0.0, + early_stop_eps=0, + ) + else: + + def sigma_fn(t_starts, t_ends, ray_indices): + t_starts, t_ends = t_starts[..., None], t_ends[..., None] + t_origins = rays_o_flatten[ray_indices] + t_positions = (t_starts + t_ends) / 2.0 + t_dirs = rays_d_flatten[ray_indices] + positions = t_origins + t_dirs * t_positions + if self.training: + sigma = self.geometry.forward_density(positions)[..., 0] + else: + sigma = chunk_batch( + self.geometry.forward_density, + self.cfg.eval_chunk_size, + positions, + )[..., 0] + return sigma + + with torch.no_grad(): + ray_indices, t_starts_, t_ends_ = self.estimator.sampling( + rays_o_flatten, + rays_d_flatten, + sigma_fn=sigma_fn if self.cfg.prune_alpha_threshold else None, + near_plane=self.cfg.near_plane, + far_plane=self.cfg.far_plane, + render_step_size=self.render_step_size, + alpha_thre=0.01 if self.cfg.prune_alpha_threshold else 0.0, + stratified=self.randomized, + cone_angle=0.0, + ) + elif self.cfg.estimator == "proposal": + + def prop_sigma_fn( + t_starts: Float[Tensor, "Nr Ns"], + t_ends: Float[Tensor, "Nr Ns"], + proposal_network, + ): + t_origins: Float[Tensor, "Nr 1 3"] = rays_o_flatten.unsqueeze(-2) + t_dirs: Float[Tensor, "Nr 1 3"] = rays_d_flatten.unsqueeze(-2) + positions: Float[Tensor, "Nr Ns 3"] = ( + t_origins + t_dirs * (t_starts + t_ends)[..., None] / 2.0 + ) + aabb_min, aabb_max = self.bbox[0], self.bbox[1] + positions = (positions - aabb_min) / (aabb_max - aabb_min) + selector = ((positions > 0.0) & (positions < 1.0)).all(dim=-1) + density_before_activation = ( + proposal_network(positions.view(-1, 3)) + .view(*positions.shape[:-1], 1) + .to(positions) + ) + density: Float[Tensor, "Nr Ns 1"] = ( + get_activation("shifted_trunc_exp")(density_before_activation) + * selector[..., None] + ) + return density.squeeze(-1) + + t_starts_, t_ends_ = self.estimator.sampling( + prop_sigma_fns=[partial(prop_sigma_fn, proposal_network=self.prop_net)], + prop_samples=[self.cfg.num_samples_per_ray_proposal], + num_samples=self.cfg.num_samples_per_ray, + n_rays=n_rays, + near_plane=self.cfg.near_plane, + far_plane=self.cfg.far_plane, + sampling_type="uniform", + stratified=self.randomized, + requires_grad=self.vars_in_forward["requires_grad"], + ) + ray_indices = ( + torch.arange(n_rays, device=rays_o_flatten.device) + .unsqueeze(-1) + .expand(-1, t_starts_.shape[1]) + ) + ray_indices = ray_indices.flatten() + t_starts_ = t_starts_.flatten() + t_ends_ = t_ends_.flatten() + elif self.cfg.estimator == "importance": + + def prop_sigma_fn( + t_starts: Float[Tensor, "Nr Ns"], + t_ends: Float[Tensor, "Nr Ns"], + proposal_network, + ): + t_origins: Float[Tensor, "Nr 1 3"] = rays_o_flatten.unsqueeze(-2) + t_dirs: Float[Tensor, "Nr 1 3"] = rays_d_flatten.unsqueeze(-2) + positions: Float[Tensor, "Nr Ns 3"] = ( + t_origins + t_dirs * (t_starts + t_ends)[..., None] / 2.0 + ) + with torch.no_grad(): + geo_out = chunk_batch( + proposal_network, + self.cfg.eval_chunk_size, + positions.reshape(-1, 3), + output_normal=False, + ) + density = geo_out["density"] + return density.reshape(positions.shape[:2]) + + t_starts_, t_ends_ = self.estimator.sampling( + prop_sigma_fns=[partial(prop_sigma_fn, proposal_network=self.geometry)], + prop_samples=[self.cfg.num_samples_per_ray_importance], + num_samples=self.cfg.num_samples_per_ray, + n_rays=n_rays, + near_plane=self.cfg.near_plane, + far_plane=self.cfg.far_plane, + sampling_type="uniform", + stratified=self.randomized, + ) + ray_indices = ( + torch.arange(n_rays, device=rays_o_flatten.device) + .unsqueeze(-1) + .expand(-1, t_starts_.shape[1]) + ) + ray_indices = ray_indices.flatten() + t_starts_ = t_starts_.flatten() + t_ends_ = t_ends_.flatten() + else: + raise NotImplementedError + + ray_indices, t_starts_, t_ends_ = validate_empty_rays( + ray_indices, t_starts_, t_ends_ + ) + ray_indices = ray_indices.long() + t_starts, t_ends = t_starts_[..., None], t_ends_[..., None] + t_origins = rays_o_flatten[ray_indices] + t_dirs = rays_d_flatten[ray_indices] + t_light_positions = light_positions_flatten[ray_indices] + t_positions = (t_starts + t_ends) / 2.0 + positions = t_origins + t_dirs * t_positions + t_intervals = t_ends - t_starts + + if self.training: + geo_out = self.geometry( + positions, output_normal=self.material.requires_normal + ) + rgb_fg_all = self.material( + viewdirs=t_dirs, + positions=positions, + light_positions=t_light_positions, + **geo_out, + **kwargs + ) + comp_rgb_bg = self.background(dirs=rays_d) + else: + geo_out = chunk_batch( + self.geometry, + self.cfg.eval_chunk_size, + positions, + output_normal=self.material.requires_normal, + ) + rgb_fg_all = chunk_batch( + self.material, + self.cfg.eval_chunk_size, + viewdirs=t_dirs, + positions=positions, + light_positions=t_light_positions, + **geo_out + ) + comp_rgb_bg = chunk_batch( + self.background, self.cfg.eval_chunk_size, dirs=rays_d + ) + + weights: Float[Tensor, "Nr 1"] + weights_, trans_, _ = nerfacc.render_weight_from_density( + t_starts[..., 0], + t_ends[..., 0], + geo_out["density"][..., 0], + ray_indices=ray_indices, + n_rays=n_rays, + ) + if self.training and self.cfg.estimator == "proposal": + self.vars_in_forward["trans"] = trans_.reshape(n_rays, -1) + + weights = weights_[..., None] + opacity: Float[Tensor, "Nr 1"] = nerfacc.accumulate_along_rays( + weights[..., 0], values=None, ray_indices=ray_indices, n_rays=n_rays + ) + depth: Float[Tensor, "Nr 1"] = nerfacc.accumulate_along_rays( + weights[..., 0], values=t_positions, ray_indices=ray_indices, n_rays=n_rays + ) + comp_rgb_fg: Float[Tensor, "Nr Nc"] = nerfacc.accumulate_along_rays( + weights[..., 0], values=rgb_fg_all, ray_indices=ray_indices, n_rays=n_rays + ) + + # populate depth and opacity to each point + weights_normalized = weights / opacity.clamp(min=1e-5)[ray_indices] # num_pts + # z-variance loss from HiFA: https://hifa-team.github.io/HiFA-site/ + z_mean: Float[Tensor, "Nr 1"] = nerfacc.accumulate_along_rays( + weights_normalized[..., 0], + values=t_positions, + ray_indices=ray_indices, + n_rays=n_rays, + ) + z_variance_unmasked = nerfacc.accumulate_along_rays( + weights_normalized[..., 0], + values=(t_positions - z_mean[ray_indices]) ** 2, + ray_indices=ray_indices, + n_rays=n_rays, + ) + z_variance = z_variance_unmasked * (opacity > 0.5).float() + + if bg_color is None: + bg_color = comp_rgb_bg + else: + if bg_color.shape[:-1] == (batch_size,): + # e.g. constant random color used for Zero123 + # [bs,3] -> [bs, 1, 1, 3]): + bg_color = bg_color.unsqueeze(1).unsqueeze(1) + # -> [bs, height, width, 3]): + bg_color = bg_color.expand(-1, height, width, -1) + + if bg_color.shape[:-1] == (batch_size, height, width): + bg_color = bg_color.reshape(batch_size * height * width, -1) + + comp_rgb = comp_rgb_fg + bg_color * (1.0 - opacity) + + out = { + "comp_rgb": comp_rgb.view(batch_size, height, width, -1), + "comp_rgb_fg": comp_rgb_fg.view(batch_size, height, width, -1), + "comp_rgb_bg": comp_rgb_bg.view(batch_size, height, width, -1), + "opacity": opacity.view(batch_size, height, width, 1), + "depth": depth.view(batch_size, height, width, 1), + "z_variance": z_variance.view(batch_size, height, width, 1), + } + + if self.training: + out.update( + { + "weights": weights, + "t_points": t_positions, + "t_intervals": t_intervals, + "t_dirs": t_dirs, + "ray_indices": ray_indices, + "points": positions, + **geo_out, + } + ) + if "normal" in geo_out: + if self.cfg.return_comp_normal: + comp_normal: Float[Tensor, "Nr 3"] = nerfacc.accumulate_along_rays( + weights[..., 0], + values=geo_out["normal"], + ray_indices=ray_indices, + n_rays=n_rays, + ) + comp_normal = F.normalize(comp_normal, dim=-1) + comp_normal = ( + (comp_normal + 1.0) / 2.0 * opacity + ) # for visualization + out.update( + { + "comp_normal": comp_normal.view( + batch_size, height, width, 3 + ), + } + ) + if self.cfg.return_normal_perturb: + normal_perturb = self.geometry( + positions + torch.randn_like(positions) * 1e-2, + output_normal=self.material.requires_normal, + )["normal"] + out.update({"normal_perturb": normal_perturb}) + else: + if "normal" in geo_out: + comp_normal = nerfacc.accumulate_along_rays( + weights[..., 0], + values=geo_out["normal"], + ray_indices=ray_indices, + n_rays=n_rays, + ) + comp_normal = F.normalize(comp_normal, dim=-1) + comp_normal = (comp_normal + 1.0) / 2.0 * opacity # for visualization + out.update( + { + "comp_normal": comp_normal.view(batch_size, height, width, 3), + } + ) + + return out + + def update_step( + self, epoch: int, global_step: int, on_load_weights: bool = False + ) -> None: + if self.cfg.estimator == "occgrid": + if self.cfg.grid_prune: + + def occ_eval_fn(x): + density = self.geometry.forward_density(x) + # approximate for 1 - torch.exp(-density * self.render_step_size) based on taylor series + return density * self.render_step_size + + if self.training and not on_load_weights: + self.estimator.update_every_n_steps( + step=global_step, occ_eval_fn=occ_eval_fn + ) + elif self.cfg.estimator == "proposal": + if self.training: + requires_grad = self.proposal_requires_grad_fn(global_step) + self.vars_in_forward["requires_grad"] = requires_grad + else: + self.vars_in_forward["requires_grad"] = False + + def update_step_end(self, epoch: int, global_step: int) -> None: + if self.cfg.estimator == "proposal" and self.training: + self.estimator.update_every_n_steps( + self.vars_in_forward["trans"], + self.vars_in_forward["requires_grad"], + loss_scaler=1.0, + ) + + def train(self, mode=True): + self.randomized = mode and self.cfg.randomized + if self.cfg.estimator == "proposal": + self.prop_net.train() + return super().train(mode=mode) + + def eval(self): + self.randomized = False + if self.cfg.estimator == "proposal": + self.prop_net.eval() + return super().eval() diff --git a/threestudio/models/renderers/neus_volume_renderer.py b/threestudio/models/renderers/neus_volume_renderer.py new file mode 100644 index 0000000..0960176 --- /dev/null +++ b/threestudio/models/renderers/neus_volume_renderer.py @@ -0,0 +1,390 @@ +from dataclasses import dataclass +from functools import partial + +import nerfacc +import torch +import torch.nn as nn +import torch.nn.functional as F + +import threestudio +from threestudio.models.background.base import BaseBackground +from threestudio.models.estimators import ImportanceEstimator +from threestudio.models.geometry.base import BaseImplicitGeometry +from threestudio.models.materials.base import BaseMaterial +from threestudio.models.renderers.base import VolumeRenderer +from threestudio.utils.ops import chunk_batch, validate_empty_rays +from threestudio.utils.typing import * + + +def volsdf_density(sdf, inv_std): + inv_std = inv_std.clamp(0.0, 80.0) + beta = 1 / inv_std + alpha = inv_std + return alpha * (0.5 + 0.5 * sdf.sign() * torch.expm1(-sdf.abs() / beta)) + + +class LearnedVariance(nn.Module): + def __init__(self, init_val): + super(LearnedVariance, self).__init__() + self.register_parameter("_inv_std", nn.Parameter(torch.tensor(init_val))) + + @property + def inv_std(self): + val = torch.exp(self._inv_std * 10.0) + return val + + def forward(self, x): + return torch.ones_like(x) * self.inv_std.clamp(1.0e-6, 1.0e6) + + +@threestudio.register("neus-volume-renderer") +class NeuSVolumeRenderer(VolumeRenderer): + @dataclass + class Config(VolumeRenderer.Config): + num_samples_per_ray: int = 512 + randomized: bool = True + eval_chunk_size: int = 160000 + learned_variance_init: float = 0.3 + cos_anneal_end_steps: int = 0 + use_volsdf: bool = False + + near_plane: float = 0.0 + far_plane: float = 1e10 + + # in ['occgrid', 'importance'] + estimator: str = "occgrid" + + # for occgrid + grid_prune: bool = True + prune_alpha_threshold: bool = True + + # for importance + num_samples_per_ray_importance: int = 64 + + cfg: Config + + def configure( + self, + geometry: BaseImplicitGeometry, + material: BaseMaterial, + background: BaseBackground, + ) -> None: + super().configure(geometry, material, background) + self.variance = LearnedVariance(self.cfg.learned_variance_init) + if self.cfg.estimator == "occgrid": + self.estimator = nerfacc.OccGridEstimator( + roi_aabb=self.bbox.view(-1), resolution=32, levels=1 + ) + if not self.cfg.grid_prune: + self.estimator.occs.fill_(True) + self.estimator.binaries.fill_(True) + self.render_step_size = ( + 1.732 * 2 * self.cfg.radius / self.cfg.num_samples_per_ray + ) + self.randomized = self.cfg.randomized + elif self.cfg.estimator == "importance": + self.estimator = ImportanceEstimator() + else: + raise NotImplementedError( + "unknown estimator, should be in ['occgrid', 'importance']" + ) + self.cos_anneal_ratio = 1.0 + + def get_alpha(self, sdf, normal, dirs, dists): + inv_std = self.variance(sdf) + if self.cfg.use_volsdf: + alpha = torch.abs(dists.detach()) * volsdf_density(sdf, inv_std) + else: + true_cos = (dirs * normal).sum(-1, keepdim=True) + # "cos_anneal_ratio" grows from 0 to 1 in the beginning training iterations. The anneal strategy below makes + # the cos value "not dead" at the beginning training iterations, for better convergence. + iter_cos = -( + F.relu(-true_cos * 0.5 + 0.5) * (1.0 - self.cos_anneal_ratio) + + F.relu(-true_cos) * self.cos_anneal_ratio + ) # always non-positive + + # Estimate signed distances at section points + estimated_next_sdf = sdf + iter_cos * dists * 0.5 + estimated_prev_sdf = sdf - iter_cos * dists * 0.5 + + prev_cdf = torch.sigmoid(estimated_prev_sdf * inv_std) + next_cdf = torch.sigmoid(estimated_next_sdf * inv_std) + + p = prev_cdf - next_cdf + c = prev_cdf + + alpha = ((p + 1e-5) / (c + 1e-5)).clip(0.0, 1.0) + return alpha + + def forward( + self, + rays_o: Float[Tensor, "B H W 3"], + rays_d: Float[Tensor, "B H W 3"], + light_positions: Float[Tensor, "B 3"], + bg_color: Optional[Tensor] = None, + **kwargs + ) -> Dict[str, Float[Tensor, "..."]]: + batch_size, height, width = rays_o.shape[:3] + rays_o_flatten: Float[Tensor, "Nr 3"] = rays_o.reshape(-1, 3) + rays_d_flatten: Float[Tensor, "Nr 3"] = rays_d.reshape(-1, 3) + light_positions_flatten: Float[Tensor, "Nr 3"] = ( + light_positions.reshape(-1, 1, 1, 3) + .expand(-1, height, width, -1) + .reshape(-1, 3) + ) + n_rays = rays_o_flatten.shape[0] + + if self.cfg.estimator == "occgrid": + + def alpha_fn(t_starts, t_ends, ray_indices): + t_starts, t_ends = t_starts[..., None], t_ends[..., None] + t_origins = rays_o_flatten[ray_indices] + t_positions = (t_starts + t_ends) / 2.0 + t_dirs = rays_d_flatten[ray_indices] + positions = t_origins + t_dirs * t_positions + if self.training: + sdf = self.geometry.forward_sdf(positions)[..., 0] + else: + sdf = chunk_batch( + self.geometry.forward_sdf, + self.cfg.eval_chunk_size, + positions, + )[..., 0] + + inv_std = self.variance(sdf) + if self.cfg.use_volsdf: + alpha = self.render_step_size * volsdf_density(sdf, inv_std) + else: + estimated_next_sdf = sdf - self.render_step_size * 0.5 + estimated_prev_sdf = sdf + self.render_step_size * 0.5 + prev_cdf = torch.sigmoid(estimated_prev_sdf * inv_std) + next_cdf = torch.sigmoid(estimated_next_sdf * inv_std) + p = prev_cdf - next_cdf + c = prev_cdf + alpha = ((p + 1e-5) / (c + 1e-5)).clip(0.0, 1.0) + + return alpha + + if not self.cfg.grid_prune: + with torch.no_grad(): + ray_indices, t_starts_, t_ends_ = self.estimator.sampling( + rays_o_flatten, + rays_d_flatten, + alpha_fn=None, + near_plane=self.cfg.near_plane, + far_plane=self.cfg.far_plane, + render_step_size=self.render_step_size, + alpha_thre=0.0, + stratified=self.randomized, + cone_angle=0.0, + early_stop_eps=0, + ) + else: + with torch.no_grad(): + ray_indices, t_starts_, t_ends_ = self.estimator.sampling( + rays_o_flatten, + rays_d_flatten, + alpha_fn=alpha_fn if self.cfg.prune_alpha_threshold else None, + near_plane=self.cfg.near_plane, + far_plane=self.cfg.far_plane, + render_step_size=self.render_step_size, + alpha_thre=0.01 if self.cfg.prune_alpha_threshold else 0.0, + stratified=self.randomized, + cone_angle=0.0, + ) + elif self.cfg.estimator == "importance": + + def prop_sigma_fn( + t_starts: Float[Tensor, "Nr Ns"], + t_ends: Float[Tensor, "Nr Ns"], + proposal_network, + ): + if self.cfg.use_volsdf: + t_origins: Float[Tensor, "Nr 1 3"] = rays_o_flatten.unsqueeze(-2) + t_dirs: Float[Tensor, "Nr 1 3"] = rays_d_flatten.unsqueeze(-2) + positions: Float[Tensor, "Nr Ns 3"] = ( + t_origins + t_dirs * (t_starts + t_ends)[..., None] / 2.0 + ) + with torch.no_grad(): + geo_out = chunk_batch( + proposal_network, + self.cfg.eval_chunk_size, + positions.reshape(-1, 3), + output_normal=False, + ) + inv_std = self.variance(geo_out["sdf"]) + density = volsdf_density(geo_out["sdf"], inv_std) + return density.reshape(positions.shape[:2]) + else: + raise ValueError( + "Currently only VolSDF supports importance sampling." + ) + + t_starts_, t_ends_ = self.estimator.sampling( + prop_sigma_fns=[partial(prop_sigma_fn, proposal_network=self.geometry)], + prop_samples=[self.cfg.num_samples_per_ray_importance], + num_samples=self.cfg.num_samples_per_ray, + n_rays=n_rays, + near_plane=self.cfg.near_plane, + far_plane=self.cfg.far_plane, + sampling_type="uniform", + stratified=self.randomized, + ) + ray_indices = ( + torch.arange(n_rays, device=rays_o_flatten.device) + .unsqueeze(-1) + .expand(-1, t_starts_.shape[1]) + ) + ray_indices = ray_indices.flatten() + t_starts_ = t_starts_.flatten() + t_ends_ = t_ends_.flatten() + else: + raise NotImplementedError + + ray_indices, t_starts_, t_ends_ = validate_empty_rays( + ray_indices, t_starts_, t_ends_ + ) + ray_indices = ray_indices.long() + t_starts, t_ends = t_starts_[..., None], t_ends_[..., None] + t_origins = rays_o_flatten[ray_indices] + t_dirs = rays_d_flatten[ray_indices] + t_light_positions = light_positions_flatten[ray_indices] + t_positions = (t_starts + t_ends) / 2.0 + positions = t_origins + t_dirs * t_positions + t_intervals = t_ends - t_starts + + if self.training: + geo_out = self.geometry(positions, output_normal=True) + rgb_fg_all = self.material( + viewdirs=t_dirs, + positions=positions, + light_positions=t_light_positions, + **geo_out, + **kwargs + ) + comp_rgb_bg = self.background(dirs=rays_d) + else: + geo_out = chunk_batch( + self.geometry, + self.cfg.eval_chunk_size, + positions, + output_normal=True, + ) + rgb_fg_all = chunk_batch( + self.material, + self.cfg.eval_chunk_size, + viewdirs=t_dirs, + positions=positions, + light_positions=t_light_positions, + **geo_out + ) + comp_rgb_bg = chunk_batch( + self.background, self.cfg.eval_chunk_size, dirs=rays_d + ) + + # grad or normal? + alpha: Float[Tensor, "Nr 1"] = self.get_alpha( + geo_out["sdf"], geo_out["normal"], t_dirs, t_intervals + ) + + weights: Float[Tensor, "Nr 1"] + weights_, _ = nerfacc.render_weight_from_alpha( + alpha[..., 0], + ray_indices=ray_indices, + n_rays=n_rays, + ) + weights = weights_[..., None] + opacity: Float[Tensor, "Nr 1"] = nerfacc.accumulate_along_rays( + weights[..., 0], values=None, ray_indices=ray_indices, n_rays=n_rays + ) + depth: Float[Tensor, "Nr 1"] = nerfacc.accumulate_along_rays( + weights[..., 0], values=t_positions, ray_indices=ray_indices, n_rays=n_rays + ) + comp_rgb_fg: Float[Tensor, "Nr Nc"] = nerfacc.accumulate_along_rays( + weights[..., 0], values=rgb_fg_all, ray_indices=ray_indices, n_rays=n_rays + ) + + if bg_color is None: + bg_color = comp_rgb_bg + + if bg_color.shape[:-1] == (batch_size, height, width): + bg_color = bg_color.reshape(batch_size * height * width, -1) + + comp_rgb = comp_rgb_fg + bg_color * (1.0 - opacity) + + out = { + "comp_rgb": comp_rgb.view(batch_size, height, width, -1), + "comp_rgb_fg": comp_rgb_fg.view(batch_size, height, width, -1), + "comp_rgb_bg": comp_rgb_bg.view(batch_size, height, width, -1), + "opacity": opacity.view(batch_size, height, width, 1), + "depth": depth.view(batch_size, height, width, 1), + } + + if self.training: + out.update( + { + "weights": weights, + "t_points": t_positions, + "t_intervals": t_intervals, + "t_dirs": t_dirs, + "ray_indices": ray_indices, + "points": positions, + **geo_out, + } + ) + else: + if "normal" in geo_out: + comp_normal: Float[Tensor, "Nr 3"] = nerfacc.accumulate_along_rays( + weights[..., 0], + values=geo_out["normal"], + ray_indices=ray_indices, + n_rays=n_rays, + ) + comp_normal = F.normalize(comp_normal, dim=-1) + comp_normal = (comp_normal + 1.0) / 2.0 * opacity # for visualization + out.update( + { + "comp_normal": comp_normal.view(batch_size, height, width, 3), + } + ) + out.update({"inv_std": self.variance.inv_std}) + return out + + def update_step( + self, epoch: int, global_step: int, on_load_weights: bool = False + ) -> None: + self.cos_anneal_ratio = ( + 1.0 + if self.cfg.cos_anneal_end_steps == 0 + else min(1.0, global_step / self.cfg.cos_anneal_end_steps) + ) + if self.cfg.estimator == "occgrid": + if self.cfg.grid_prune: + + def occ_eval_fn(x): + sdf = self.geometry.forward_sdf(x) + inv_std = self.variance(sdf) + if self.cfg.use_volsdf: + alpha = self.render_step_size * volsdf_density(sdf, inv_std) + else: + estimated_next_sdf = sdf - self.render_step_size * 0.5 + estimated_prev_sdf = sdf + self.render_step_size * 0.5 + prev_cdf = torch.sigmoid(estimated_prev_sdf * inv_std) + next_cdf = torch.sigmoid(estimated_next_sdf * inv_std) + p = prev_cdf - next_cdf + c = prev_cdf + alpha = ((p + 1e-5) / (c + 1e-5)).clip(0.0, 1.0) + return alpha + + if self.training and not on_load_weights: + self.estimator.update_every_n_steps( + step=global_step, occ_eval_fn=occ_eval_fn + ) + + def train(self, mode=True): + self.randomized = mode and self.cfg.randomized + return super().train(mode=mode) + + def eval(self): + self.randomized = False + return super().eval() diff --git a/threestudio/models/renderers/nvdiff_rasterizer.py b/threestudio/models/renderers/nvdiff_rasterizer.py new file mode 100644 index 0000000..9f83f7f --- /dev/null +++ b/threestudio/models/renderers/nvdiff_rasterizer.py @@ -0,0 +1,111 @@ +from dataclasses import dataclass + +import nerfacc +import torch +import torch.nn.functional as F + +import threestudio +from threestudio.models.background.base import BaseBackground +from threestudio.models.geometry.base import BaseImplicitGeometry +from threestudio.models.materials.base import BaseMaterial +from threestudio.models.renderers.base import Rasterizer, VolumeRenderer +from threestudio.utils.misc import get_device +from threestudio.utils.rasterize import NVDiffRasterizerContext +from threestudio.utils.typing import * + + +@threestudio.register("nvdiff-rasterizer") +class NVDiffRasterizer(Rasterizer): + @dataclass + class Config(VolumeRenderer.Config): + context_type: str = "gl" + + cfg: Config + + def configure( + self, + geometry: BaseImplicitGeometry, + material: BaseMaterial, + background: BaseBackground, + ) -> None: + super().configure(geometry, material, background) + self.ctx = NVDiffRasterizerContext(self.cfg.context_type, get_device()) + + def forward( + self, + mvp_mtx: Float[Tensor, "B 4 4"], + camera_positions: Float[Tensor, "B 3"], + light_positions: Float[Tensor, "B 3"], + height: int, + width: int, + render_rgb: bool = True, + **kwargs + ) -> Dict[str, Any]: + batch_size = mvp_mtx.shape[0] + mesh = self.geometry.isosurface() + + v_pos_clip: Float[Tensor, "B Nv 4"] = self.ctx.vertex_transform( + mesh.v_pos, mvp_mtx + ) + rast, _ = self.ctx.rasterize(v_pos_clip, mesh.t_pos_idx, (height, width)) + mask = rast[..., 3:] > 0 + mask_aa = self.ctx.antialias(mask.float(), rast, v_pos_clip, mesh.t_pos_idx) + + out = {"opacity": mask_aa, "mesh": mesh} + + gb_normal, _ = self.ctx.interpolate_one(mesh.v_nrm, rast, mesh.t_pos_idx) + gb_normal = F.normalize(gb_normal, dim=-1) + gb_normal_aa = torch.lerp( + torch.zeros_like(gb_normal), (gb_normal + 1.0) / 2.0, mask.float() + ) + gb_normal_aa = self.ctx.antialias( + gb_normal_aa, rast, v_pos_clip, mesh.t_pos_idx + ) + out.update({"comp_normal": gb_normal_aa}) # in [0, 1] + + # TODO: make it clear whether to compute the normal, now we compute it in all cases + # consider using: require_normal_computation = render_normal or (render_rgb and material.requires_normal) + # or + # render_normal = render_normal or (render_rgb and material.requires_normal) + + if render_rgb: + selector = mask[..., 0] + + gb_pos, _ = self.ctx.interpolate_one(mesh.v_pos, rast, mesh.t_pos_idx) + gb_viewdirs = F.normalize( + gb_pos - camera_positions[:, None, None, :], dim=-1 + ) + gb_light_positions = light_positions[:, None, None, :].expand( + -1, height, width, -1 + ) + + positions = gb_pos[selector] + geo_out = self.geometry(positions, output_normal=False) + + extra_geo_info = {} + if self.material.requires_normal: + extra_geo_info["shading_normal"] = gb_normal[selector] + if self.material.requires_tangent: + gb_tangent, _ = self.ctx.interpolate_one( + mesh.v_tng, rast, mesh.t_pos_idx + ) + gb_tangent = F.normalize(gb_tangent, dim=-1) + extra_geo_info["tangent"] = gb_tangent[selector] + + rgb_fg = self.material( + viewdirs=gb_viewdirs[selector], + positions=positions, + light_positions=gb_light_positions[selector], + **extra_geo_info, + **geo_out + ) + gb_rgb_fg = torch.zeros(batch_size, height, width, 3).to(rgb_fg) + gb_rgb_fg[selector] = rgb_fg + + gb_rgb_bg = self.background(dirs=gb_viewdirs) + gb_rgb = torch.lerp(gb_rgb_bg, gb_rgb_fg, mask.float()) + gb_rgb_aa = self.ctx.antialias(gb_rgb, rast, v_pos_clip, mesh.t_pos_idx) + + out.update({"comp_rgb": gb_rgb_aa, "comp_rgb_bg": gb_rgb_bg}) + + return out diff --git a/threestudio/models/renderers/patch_renderer.py b/threestudio/models/renderers/patch_renderer.py new file mode 100644 index 0000000..d595791 --- /dev/null +++ b/threestudio/models/renderers/patch_renderer.py @@ -0,0 +1,106 @@ +from dataclasses import dataclass + +import torch +import torch.nn.functional as F + +import threestudio +from threestudio.models.background.base import BaseBackground +from threestudio.models.geometry.base import BaseImplicitGeometry +from threestudio.models.materials.base import BaseMaterial +from threestudio.models.renderers.base import VolumeRenderer +from threestudio.utils.typing import * + + +@threestudio.register("patch-renderer") +class PatchRenderer(VolumeRenderer): + @dataclass + class Config(VolumeRenderer.Config): + patch_size: int = 128 + base_renderer_type: str = "" + base_renderer: Optional[VolumeRenderer.Config] = None + global_detach: bool = False + global_downsample: int = 4 + + cfg: Config + + def configure( + self, + geometry: BaseImplicitGeometry, + material: BaseMaterial, + background: BaseBackground, + ) -> None: + self.base_renderer = threestudio.find(self.cfg.base_renderer_type)( + self.cfg.base_renderer, + geometry=geometry, + material=material, + background=background, + ) + + def forward( + self, + rays_o: Float[Tensor, "B H W 3"], + rays_d: Float[Tensor, "B H W 3"], + light_positions: Float[Tensor, "B 3"], + bg_color: Optional[Tensor] = None, + **kwargs + ) -> Dict[str, Float[Tensor, "..."]]: + B, H, W, _ = rays_o.shape + + if self.base_renderer.training: + downsample = self.cfg.global_downsample + global_rays_o = torch.nn.functional.interpolate( + rays_o.permute(0, 3, 1, 2), + (H // downsample, W // downsample), + mode="bilinear", + ).permute(0, 2, 3, 1) + global_rays_d = torch.nn.functional.interpolate( + rays_d.permute(0, 3, 1, 2), + (H // downsample, W // downsample), + mode="bilinear", + ).permute(0, 2, 3, 1) + out_global = self.base_renderer( + global_rays_o, global_rays_d, light_positions, bg_color, **kwargs + ) + + PS = self.cfg.patch_size + patch_x = torch.randint(0, W - PS, (1,)).item() + patch_y = torch.randint(0, H - PS, (1,)).item() + patch_rays_o = rays_o[:, patch_y : patch_y + PS, patch_x : patch_x + PS] + patch_rays_d = rays_d[:, patch_y : patch_y + PS, patch_x : patch_x + PS] + out = self.base_renderer( + patch_rays_o, patch_rays_d, light_positions, bg_color, **kwargs + ) + + valid_patch_key = [] + for key in out: + if torch.is_tensor(out[key]): + if len(out[key].shape) == len(out["comp_rgb"].shape): + if out[key][..., 0].shape == out["comp_rgb"][..., 0].shape: + valid_patch_key.append(key) + for key in valid_patch_key: + out_global[key] = F.interpolate( + out_global[key].permute(0, 3, 1, 2), (H, W), mode="bilinear" + ).permute(0, 2, 3, 1) + if self.cfg.global_detach: + out_global[key] = out_global[key].detach() + out_global[key][ + :, patch_y : patch_y + PS, patch_x : patch_x + PS + ] = out[key] + out = out_global + else: + out = self.base_renderer( + rays_o, rays_d, light_positions, bg_color, **kwargs + ) + + return out + + def update_step( + self, epoch: int, global_step: int, on_load_weights: bool = False + ) -> None: + self.base_renderer.update_step(epoch, global_step, on_load_weights) + + def train(self, mode=True): + return self.base_renderer.train(mode) + + def eval(self): + return self.base_renderer.eval() diff --git a/threestudio/scripts/make_training_vid.py b/threestudio/scripts/make_training_vid.py new file mode 100644 index 0000000..2dee971 --- /dev/null +++ b/threestudio/scripts/make_training_vid.py @@ -0,0 +1,77 @@ +# make_training_vid("outputs/zero123/64_teddy_rgba.png@20230627-195615", frames_per_vid=30, fps=20, max_iters=200) +import argparse +import glob +import os + +import imageio +import numpy as np +from PIL import Image, ImageDraw +from tqdm import tqdm + + +def draw_text_in_image(img, texts): + img = Image.fromarray(img) + draw = ImageDraw.Draw(img) + black, white = (0, 0, 0), (255, 255, 255) + for i, text in enumerate(texts): + draw.text((2, (img.size[1] // len(texts)) * i + 1), f"{text}", white) + draw.text((0, (img.size[1] // len(texts)) * i + 1), f"{text}", white) + draw.text((2, (img.size[1] // len(texts)) * i - 1), f"{text}", white) + draw.text((0, (img.size[1] // len(texts)) * i - 1), f"{text}", white) + draw.text((1, (img.size[1] // len(texts)) * i), f"{text}", black) + return np.asarray(img) + + +def make_training_vid(exp, frames_per_vid=1, fps=3, max_iters=None, max_vids=None): + # exp = "/admin/home-vikram/git/threestudio/outputs/zero123/64_teddy_rgba.png@20230627-195615" + files = glob.glob(os.path.join(exp, "save", "*.mp4")) + if os.path.join(exp, "save", "training_vid.mp4") in files: + files.remove(os.path.join(exp, "save", "training_vid.mp4")) + its = [int(os.path.basename(file).split("-")[0].split("it")[-1]) for file in files] + it_sort = np.argsort(its) + files = list(np.array(files)[it_sort]) + its = list(np.array(its)[it_sort]) + max_vids = max_iters // its[0] if max_iters is not None else max_vids + files, its = files[:max_vids], its[:max_vids] + frames, i = [], 0 + for it, file in tqdm(zip(its, files), total=len(files)): + vid = imageio.mimread(file) + for _ in range(frames_per_vid): + frame = vid[i % len(vid)] + frame = draw_text_in_image(frame, [str(it)]) + frames.append(frame) + i += 1 + # Save + imageio.mimwrite(os.path.join(exp, "save", "training_vid.mp4"), frames, fps=fps) + + +def join(file1, file2, name): + # file1 = "/admin/home-vikram/git/threestudio/outputs/zero123/OLD_64_dragon2_rgba.png@20230629-023028/save/it200-val.mp4" + # file2 = "/admin/home-vikram/git/threestudio/outputs/zero123/64_dragon2_rgba.png@20230628-152734/save/it200-val.mp4" + vid1 = imageio.mimread(file1) + vid2 = imageio.mimread(file2) + frames = [] + for f1, f2 in zip(vid1, vid2): + frames.append( + np.concatenate([f1[:, : f1.shape[0]], f2[:, : f2.shape[0]]], axis=1) + ) + imageio.mimwrite(name, frames) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--exp", help="directory of experiment") + parser.add_argument( + "--frames_per_vid", type=int, default=1, help="# of frames from each val vid" + ) + parser.add_argument("--fps", type=int, help="max # of iters to save") + parser.add_argument("--max_iters", type=int, help="max # of iters to save") + parser.add_argument( + "--max_vids", + type=int, + help="max # of val videos to save. Will be overridden by max_iters", + ) + args = parser.parse_args() + make_training_vid( + args.exp, args.frames_per_vid, args.fps, args.max_iters, args.max_vids + ) diff --git a/threestudio/scripts/run_zero123.sh b/threestudio/scripts/run_zero123.sh new file mode 100755 index 0000000..6f21bd7 --- /dev/null +++ b/threestudio/scripts/run_zero123.sh @@ -0,0 +1,13 @@ +NAME="dragon2" + +# Phase 1 - 64x64 +python launch.py --config configs/zero123.yaml --train --gpu 7 data.image_path=./load/images/${NAME}_rgba.png use_timestamp=False name=${NAME} tag=Phase1 # system.freq.guidance_eval=0 system.loggers.wandb.enable=false system.loggers.wandb.project="zero123" system.loggers.wandb.name=${NAME}_Phase1 + +# Phase 1.5 - 512 refine +python launch.py --config configs/zero123-geometry.yaml --train --gpu 4 data.image_path=./load/images/${NAME}_rgba.png system.geometry_convert_from=./outputs/${NAME}/Phase1/ckpts/last.ckpt use_timestamp=False name=${NAME} tag=Phase1p5 # system.freq.guidance_eval=0 system.loggers.wandb.enable=false system.loggers.wandb.project="zero123" system.loggers.wandb.name=${NAME}_Phase1p5 + +# Phase 2 - dreamfusion +python launch.py --config configs/experimental/imagecondition_zero123nerf.yaml --train --gpu 5 data.image_path=./load/images/${NAME}_rgba.png system.prompt_processor.prompt="A 3D model of a friendly dragon" system.weights="/admin/home-vikram/git/threestudio/outputs/${NAME}/Phase1/ckpts/last.ckpt" name=${NAME} tag=Phase2 # system.freq.guidance_eval=0 system.loggers.wandb.enable=false system.loggers.wandb.project="zero123" system.loggers.wandb.name=${NAME}_Phase2 + +# Phase 2 - SDF + dreamfusion +python launch.py --config configs/experimental/imagecondition_zero123nerf_refine.yaml --train --gpu 5 data.image_path=./load/images/${NAME}_rgba.png system.prompt_processor.prompt="A 3D model of a friendly dragon" system.geometry_convert_from="/admin/home-vikram/git/threestudio/outputs/${NAME}/Phase1/ckpts/last.ckpt" name=${NAME} tag=Phase2_refine # system.freq.guidance_eval=0 system.loggers.wandb.enable=false system.loggers.wandb.project="zero123" system.loggers.wandb.name=${NAME}_Phase2_refine diff --git a/threestudio/scripts/run_zero123_comparison.sh b/threestudio/scripts/run_zero123_comparison.sh new file mode 100755 index 0000000..7f9f60a --- /dev/null +++ b/threestudio/scripts/run_zero123_comparison.sh @@ -0,0 +1,23 @@ +# with standard zero123 +threestudio/scripts/run_zero123_phase.sh 6 anya_front 105000 0 + +# with zero123XL (not released yet!) +threestudio/scripts/run_zero123_phase.sh 1 anya_front XL_20230604 0 +threestudio/scripts/run_zero123_phase.sh 2 baby_phoenix_on_ice XL_20230604 20 +threestudio/scripts/run_zero123_phase.sh 3 beach_house_1 XL_20230604 50 +threestudio/scripts/run_zero123_phase.sh 4 bollywood_actress XL_20230604 0 +threestudio/scripts/run_zero123_phase.sh 5 beach_house_2 XL_20230604 30 +threestudio/scripts/run_zero123_phase.sh 6 hamburger XL_20230604 10 +threestudio/scripts/run_zero123_phase.sh 7 cactus XL_20230604 8 +threestudio/scripts/run_zero123_phase.sh 0 catstatue XL_20230604 50 +threestudio/scripts/run_zero123_phase.sh 1 church_ruins XL_20230604 0 +threestudio/scripts/run_zero123_phase.sh 2 firekeeper XL_20230604 10 +threestudio/scripts/run_zero123_phase.sh 3 futuristic_car XL_20230604 20 +threestudio/scripts/run_zero123_phase.sh 4 mona_lisa XL_20230604 10 +threestudio/scripts/run_zero123_phase.sh 5 teddy XL_20230604 20 + +# set guidance_eval to 0, to greatly speed up training +threestudio/scripts/run_zero123_phase.sh 7 anya_front XL_20230604 0 system.freq.guidance_eval=0 + +# disable wandb for faster training (or if you don't want to use it) +threestudio/scripts/run_zero123_phase.sh 7 anya_front XL_20230604 0 system.loggers.wandb.enable=false system.freq.guidance_eval=0 diff --git a/threestudio/scripts/run_zero123_phase.sh b/threestudio/scripts/run_zero123_phase.sh new file mode 100755 index 0000000..03c1461 --- /dev/null +++ b/threestudio/scripts/run_zero123_phase.sh @@ -0,0 +1,14 @@ + +GPU_ID=$1 # e.g. 0 +IMAGE_PREFIX=$2 # e.g. "anya_front" +ZERO123_PREFIX=$3 # e.g. "zero123-xl" +ELEVATION=$4 # e.g. 0 +REST=${@:5:99} # e.g. "system.guidance.min_step_percent=0.1 system.guidance.max_step_percent=0.9" + +# change this config if you don't use wandb or want to speed up training +python launch.py --config configs/zero123.yaml --train --gpu $GPU_ID system.loggers.wandb.enable=true system.loggers.wandb.project="claforte-noise_atten" \ + system.loggers.wandb.name="${IMAGE_PREFIX}_zero123_${ZERO123_PREFIX}...fov20_${REST}" \ + data.image_path=./load/images/${IMAGE_PREFIX}_rgba.png system.freq.guidance_eval=37 \ + system.guidance.pretrained_model_name_or_path="./load/zero123/${ZERO123_PREFIX}.ckpt" \ + system.guidance.cond_elevation_deg=$ELEVATION \ + ${REST} diff --git a/threestudio/scripts/run_zero123_phase2.sh b/threestudio/scripts/run_zero123_phase2.sh new file mode 100644 index 0000000..c6b9642 --- /dev/null +++ b/threestudio/scripts/run_zero123_phase2.sh @@ -0,0 +1,5 @@ +# Reconstruct Anya using latest Zero123XL, in <2000 steps. +python launch.py --config configs/zero123.yaml --train --gpu 0 system.loggers.wandb.enable=true system.loggers.wandb.project="voletiv-anya-new" system.loggers.wandb.name="claforte_params" data.image_path=./load/images/anya_front_rgba.png system.freq.ref_or_zero123="accumulate" system.freq.guidance_eval=13 system.guidance.pretrained_model_name_or_path="./load/zero123/zero123-xl.ckpt" + +# PHASE 2 +python launch.py --config configs/experimental/imagecondition_zero123nerf.yaml --train --gpu 0 system.prompt_processor.prompt="A DSLR 3D photo of a cute anime schoolgirl stands proudly with her arms in the air, pink hair ( unreal engine 5 trending on Artstation Ghibli 4k )" system.weights=outputs/zero123/128_anya_front_rgba.png@20230623-145711/ckpts/last.ckpt system.freq.guidance_eval=13 system.loggers.wandb.enable=true system.loggers.wandb.project="voletiv-anya-new" data.image_path=./load/images/anya_front_rgba.png system.loggers.wandb.name="anya" data.random_camera.progressive_until=500 diff --git a/threestudio/scripts/run_zero123_sbatch.py b/threestudio/scripts/run_zero123_sbatch.py new file mode 100755 index 0000000..8ec2642 --- /dev/null +++ b/threestudio/scripts/run_zero123_sbatch.py @@ -0,0 +1,33 @@ +import os +import time + +files = [ + "~/git/threestudio/load/images/dog1_rgba.png", + "~/git/threestudio/load/images/dragon2_rgba.png", +] + +for file in files: + name = os.path.basename(file).split("_rgba.png")[0] + with open( + os.path.expanduser("~/git/threestudio/threestudio/scripts/zero123_sbatch.sh"), + "w", + ) as f: + f.write("#!/bin/bash\n") + f.write(f"#SBATCH --job-name=vikky_{name}\n") + f.write("#SBATCH --account=mod3d\n") + f.write("#SBATCH --partition=g40\n") + f.write("#SBATCH --gpus=1\n") + f.write("#SBATCH --time=0-00:07:00\n") + f.write("conda activate three\n") + f.write("cd ~/git/threestudio/\n") + f.write(f"NAME={name}\n") + # Phase 1 + f.write( + "python launch.py --config configs/zero123.yaml --train data.image_path=./load/images/${NAME}_rgba.png use_timestamp=true name=${NAME} tag=Phase1 system.loggers.wandb.enable=false system.loggers.wandb.project='zero123' system.loggers.wandb.name=${NAME}_Phase1\n" + ) + # # Phase 1.5 + # f.write( + # "python launch.py --config configs/zero123-geometry.yaml --train data.image_path=./load/images/${NAME}_rgba.png system.geometry_convert_from=./outputs/${NAME}/Phase1/ckpts/last.ckpt use_timestamp=False name=${NAME} tag=Phase1p5 system.loggers.wandb.enable=true system.loggers.wandb.project='zero123' system.loggers.wandb.name=${NAME}_Phase1p5\n" + # ) + os.system("sbatch ~/git/threestudio/threestudio/scripts/zero123_sbatch.sh") + time.sleep(1) diff --git a/threestudio/scripts/zero123_demo.py b/threestudio/scripts/zero123_demo.py new file mode 100644 index 0000000..03c9e5f --- /dev/null +++ b/threestudio/scripts/zero123_demo.py @@ -0,0 +1,61 @@ +# 1. Generate using StableDiffusionXL https://clipdrop.co/stable-diffusion + +# 2. Remove background https://clipdrop.co/remove-background + +# 3. Resize to 512x512 https://www.iloveimg.com/resize-image + +# (OPTIONAL) +# 4. Estimate depth and normal https://omnidata.vision/demo/ (I used Omnidata Normal (with X-TC & 3DCC), and MiDaS Depth) + + +# (OPTIONAL) +# 5. Convert depth image from RGB to greyscale +def depth_rgb_to_grey(depth_filename): + # depth_filename = "image_depth.png" + import cv2 + import numpy as np + + # import shutil + # shutil.copyfile(depth_filename, depth_filename.replace("_depth", "_depth_orig")) + depth = cv2.imread(depth_filename) + depth = cv2.cvtColor(depth, cv2.COLOR_BGR2GRAY) + mask = ( + cv2.resize( + cv2.imread(depth_filename.replace("_depth", "_rgba"), cv2.IMREAD_UNCHANGED)[ + :, :, -1 + ], + depth.shape, + ) + > 0 + ) + # depth[mask] = (depth[mask] - depth.min()) / (depth.max() - depth.min() + 1e-9) + depth = (depth - depth.min()) / (depth.max() - depth.min() + 1e-9) + depth[~mask] = 0 + depth = (depth * 255).astype(np.uint8) + cv2.imwrite(depth_filename, depth) + + +# (OPTIONAL) +# 6. Mask normal +def normal_mask(normal_filename): + # filename = "image_normal.png" + import cv2 + + # import shutil + # shutil.copyfile(normal_filename, normal_filename.replace("_normal", "_normal_orig")) + normal = cv2.imread(normal_filename) + mask = ( + cv2.resize( + cv2.imread( + normal_filename.replace("_normal", "_rgba"), cv2.IMREAD_UNCHANGED + )[:, :, -1], + normal.shape[:2], + ) + > 0 + ) + normal[~mask] = 0 + cv2.imwrite(normal_filename, normal) + + +# 5. Run Zero123 +# python launch.py --config configs/zero123.yaml --train data.image_path=./load/images/grootplant_rgba.png diff --git a/threestudio/scripts/zero123_sbatch.sh b/threestudio/scripts/zero123_sbatch.sh new file mode 100755 index 0000000..bac2341 --- /dev/null +++ b/threestudio/scripts/zero123_sbatch.sh @@ -0,0 +1,10 @@ +#!/bin/bash +#SBATCH --job-name=vikky +#SBATCH --account=mod3d +#SBATCH --partition=g40 +#SBATCH --gpus=1 +#SBATCH --time=0-00:07:00 +conda activate three +cd ~/git/threestudio/ +NAME="dog1" +python launch.py --config configs/zero123.yaml --train data.image_path=./load/images/${NAME}_rgba.png use_timestamp=False name=${NAME} tag=Phase1 system.loggers.wandb.enable=true system.loggers.wandb.project='zero123' system.loggers.wandb.name=${NAME}_Phase1 diff --git a/threestudio/systems/__init__.py b/threestudio/systems/__init__.py new file mode 100644 index 0000000..3da7dd6 --- /dev/null +++ b/threestudio/systems/__init__.py @@ -0,0 +1,16 @@ +from . import ( + control4d_multiview, + dreamfusion, + eff_dreamfusion, + fantasia3d, + imagedreamfusion, + instructnerf2nerf, + latentnerf, + magic3d, + magic123, + prolificdreamer, + sjc, + textmesh, + zero123, + zero123_simple, +) diff --git a/threestudio/systems/base.py b/threestudio/systems/base.py new file mode 100644 index 0000000..73faac6 --- /dev/null +++ b/threestudio/systems/base.py @@ -0,0 +1,402 @@ +import os +from dataclasses import dataclass, field + +import pytorch_lightning as pl +import torch.nn.functional as F + +import threestudio +from threestudio.models.exporters.base import Exporter, ExporterOutput +from threestudio.systems.utils import parse_optimizer, parse_scheduler +from threestudio.utils.base import ( + Updateable, + update_end_if_possible, + update_if_possible, +) +from threestudio.utils.config import parse_structured +from threestudio.utils.misc import ( + C, + cleanup, + find_last_path, + get_device, + load_module_weights, +) +from threestudio.utils.saving import SaverMixin +from threestudio.utils.typing import * + + +class BaseSystem(pl.LightningModule, Updateable, SaverMixin): + @dataclass + class Config: + loggers: dict = field(default_factory=dict) + loss: dict = field(default_factory=dict) + optimizer: dict = field(default_factory=dict) + scheduler: Optional[dict] = None + weights: Optional[str] = None + weights_ignore_modules: Optional[List[str]] = None + cleanup_after_validation_step: bool = False + cleanup_after_test_step: bool = False + + cfg: Config + + def __init__(self, cfg, resumed=False) -> None: + super().__init__() + self.cfg = parse_structured(self.Config, cfg) + self._save_dir: Optional[str] = None + self._resumed: bool = resumed + self._resumed_eval: bool = False + self._resumed_eval_status: dict = {"global_step": 0, "current_epoch": 0} + if "loggers" in cfg: + self.create_loggers(cfg.loggers) + + self.configure() + if self.cfg.weights is not None: + self.load_weights(self.cfg.weights, self.cfg.weights_ignore_modules) + self.post_configure() + + def load_weights(self, weights: str, ignore_modules: Optional[List[str]] = None): + state_dict, epoch, global_step = load_module_weights( + weights, ignore_modules=ignore_modules, map_location="cpu" + ) + self.load_state_dict(state_dict, strict=False) + # restore step-dependent states + self.do_update_step(epoch, global_step, on_load_weights=True) + + def set_resume_status(self, current_epoch: int, global_step: int): + # restore correct epoch and global step in eval + self._resumed_eval = True + self._resumed_eval_status["current_epoch"] = current_epoch + self._resumed_eval_status["global_step"] = global_step + + @property + def resumed(self): + # whether from resumed checkpoint + return self._resumed + + @property + def true_global_step(self): + if self._resumed_eval: + return self._resumed_eval_status["global_step"] + else: + return self.global_step + + @property + def true_current_epoch(self): + if self._resumed_eval: + return self._resumed_eval_status["current_epoch"] + else: + return self.current_epoch + + def configure(self) -> None: + pass + + def post_configure(self) -> None: + """ + executed after weights are loaded + """ + pass + + def C(self, value: Any) -> float: + return C(value, self.true_current_epoch, self.true_global_step) + + def configure_optimizers(self): + optim = parse_optimizer(self.cfg.optimizer, self) + ret = { + "optimizer": optim, + } + if self.cfg.scheduler is not None: + ret.update( + { + "lr_scheduler": parse_scheduler(self.cfg.scheduler, optim), + } + ) + return ret + + def training_step(self, batch, batch_idx): + raise NotImplementedError + + def validation_step(self, batch, batch_idx): + raise NotImplementedError + + def on_train_batch_end(self, outputs, batch, batch_idx): + self.dataset = self.trainer.train_dataloader.dataset + update_end_if_possible( + self.dataset, self.true_current_epoch, self.true_global_step + ) + self.do_update_step_end(self.true_current_epoch, self.true_global_step) + + def on_validation_batch_end(self, outputs, batch, batch_idx): + self.dataset = self.trainer.val_dataloaders.dataset + update_end_if_possible( + self.dataset, self.true_current_epoch, self.true_global_step + ) + self.do_update_step_end(self.true_current_epoch, self.true_global_step) + if self.cfg.cleanup_after_validation_step: + # cleanup to save vram + cleanup() + + def on_validation_epoch_end(self): + raise NotImplementedError + + def test_step(self, batch, batch_idx): + raise NotImplementedError + + def on_test_batch_end(self, outputs, batch, batch_idx): + self.dataset = self.trainer.test_dataloaders.dataset + update_end_if_possible( + self.dataset, self.true_current_epoch, self.true_global_step + ) + self.do_update_step_end(self.true_current_epoch, self.true_global_step) + if self.cfg.cleanup_after_test_step: + # cleanup to save vram + cleanup() + + def on_test_epoch_end(self): + pass + + def predict_step(self, batch, batch_idx): + raise NotImplementedError + + def on_predict_batch_end(self, outputs, batch, batch_idx): + self.dataset = self.trainer.predict_dataloaders.dataset + update_end_if_possible( + self.dataset, self.true_current_epoch, self.true_global_step + ) + self.do_update_step_end(self.true_current_epoch, self.true_global_step) + if self.cfg.cleanup_after_test_step: + # cleanup to save vram + cleanup() + + def on_predict_epoch_end(self): + pass + + def preprocess_data(self, batch, stage): + pass + + """ + Implementing on_after_batch_transfer of DataModule does the same. + But on_after_batch_transfer does not support DP. + """ + + def on_train_batch_start(self, batch, batch_idx, unused=0): + self.preprocess_data(batch, "train") + self.dataset = self.trainer.train_dataloader.dataset + update_if_possible(self.dataset, self.true_current_epoch, self.true_global_step) + self.do_update_step(self.true_current_epoch, self.true_global_step) + + def on_validation_batch_start(self, batch, batch_idx, dataloader_idx=0): + self.preprocess_data(batch, "validation") + self.dataset = self.trainer.val_dataloaders.dataset + update_if_possible(self.dataset, self.true_current_epoch, self.true_global_step) + self.do_update_step(self.true_current_epoch, self.true_global_step) + + def on_test_batch_start(self, batch, batch_idx, dataloader_idx=0): + self.preprocess_data(batch, "test") + self.dataset = self.trainer.test_dataloaders.dataset + update_if_possible(self.dataset, self.true_current_epoch, self.true_global_step) + self.do_update_step(self.true_current_epoch, self.true_global_step) + + def on_predict_batch_start(self, batch, batch_idx, dataloader_idx=0): + self.preprocess_data(batch, "predict") + self.dataset = self.trainer.predict_dataloaders.dataset + update_if_possible(self.dataset, self.true_current_epoch, self.true_global_step) + self.do_update_step(self.true_current_epoch, self.true_global_step) + + def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False): + pass + + def on_before_optimizer_step(self, optimizer): + """ + # some gradient-related debugging goes here, example: + from lightning.pytorch.utilities import grad_norm + norms = grad_norm(self.geometry, norm_type=2) + print(norms) + """ + pass + + +class BaseLift3DSystem(BaseSystem): + @dataclass + class Config(BaseSystem.Config): + geometry_type: str = "" + geometry: dict = field(default_factory=dict) + geometry_convert_from: Optional[str] = None + geometry_convert_inherit_texture: bool = False + # used to override configurations of the previous geometry being converted from, + # for example isosurface_threshold + geometry_convert_override: dict = field(default_factory=dict) + + material_type: str = "" + material: dict = field(default_factory=dict) + + background_type: str = "" + background: dict = field(default_factory=dict) + + renderer_type: str = "" + renderer: dict = field(default_factory=dict) + + guidance_type: str = "" + guidance: dict = field(default_factory=dict) + + prompt_processor_type: str = "" + prompt_processor: dict = field(default_factory=dict) + + # geometry export configurations, no need to specify in training + exporter_type: str = "mesh-exporter" + exporter: dict = field(default_factory=dict) + + cfg: Config + + def configure(self) -> None: + self.cfg.geometry_convert_from = find_last_path(self.cfg.geometry_convert_from) + self.cfg.weights = find_last_path(self.cfg.weights) + if ( + self.cfg.geometry_convert_from # from_coarse must be specified + and not self.cfg.weights # not initialized from coarse when weights are specified + and not self.resumed # not initialized from coarse when resumed from checkpoints + ): + threestudio.info("Initializing geometry from a given checkpoint ...") + from threestudio.utils.config import load_config, parse_structured + + prev_cfg = load_config( + os.path.join( + os.path.dirname(self.cfg.geometry_convert_from), + "../configs/parsed.yaml", + ) + ) # TODO: hard-coded relative path + prev_system_cfg: BaseLift3DSystem.Config = parse_structured( + self.Config, prev_cfg.system + ) + prev_geometry_cfg = prev_system_cfg.geometry + prev_geometry_cfg.update(self.cfg.geometry_convert_override) + prev_geometry = threestudio.find(prev_system_cfg.geometry_type)( + prev_geometry_cfg + ) + state_dict, epoch, global_step = load_module_weights( + self.cfg.geometry_convert_from, + module_name="geometry", + map_location="cpu", + ) + prev_geometry.load_state_dict(state_dict, strict=False) + # restore step-dependent states + prev_geometry.do_update_step(epoch, global_step, on_load_weights=True) + # convert from coarse stage geometry + prev_geometry = prev_geometry.to(get_device()) + self.geometry = threestudio.find(self.cfg.geometry_type).create_from( + prev_geometry, + self.cfg.geometry, + copy_net=self.cfg.geometry_convert_inherit_texture, + ) + del prev_geometry + cleanup() + else: + self.geometry = threestudio.find(self.cfg.geometry_type)(self.cfg.geometry) + + self.material = threestudio.find(self.cfg.material_type)(self.cfg.material) + self.background = threestudio.find(self.cfg.background_type)( + self.cfg.background + ) + self.renderer = threestudio.find(self.cfg.renderer_type)( + self.cfg.renderer, + geometry=self.geometry, + material=self.material, + background=self.background, + ) + + def on_fit_start(self) -> None: + if self._save_dir is not None: + threestudio.info(f"Validation results will be saved to {self._save_dir}") + else: + threestudio.warn( + f"Saving directory not set for the system, visualization results will not be saved" + ) + + def on_test_end(self) -> None: + if self._save_dir is not None: + threestudio.info(f"Test results saved to {self._save_dir}") + + def on_predict_start(self) -> None: + self.exporter: Exporter = threestudio.find(self.cfg.exporter_type)( + self.cfg.exporter, + geometry=self.geometry, + material=self.material, + background=self.background, + ) + + def predict_step(self, batch, batch_idx): + if self.exporter.cfg.save_video: + self.test_step(batch, batch_idx) + + def on_predict_epoch_end(self) -> None: + if self.exporter.cfg.save_video: + self.on_test_epoch_end() + exporter_output: List[ExporterOutput] = self.exporter() + for out in exporter_output: + save_func_name = f"save_{out.save_type}" + if not hasattr(self, save_func_name): + raise ValueError(f"{save_func_name} not supported by the SaverMixin") + save_func = getattr(self, save_func_name) + save_func(f"it{self.true_global_step}-export/{out.save_name}", **out.params) + + def on_predict_end(self) -> None: + if self._save_dir is not None: + threestudio.info(f"Export assets saved to {self._save_dir}") + + def guidance_evaluation_save(self, comp_rgb, guidance_eval_out): + B, size = comp_rgb.shape[:2] + resize = lambda x: F.interpolate( + x.permute(0, 3, 1, 2), (size, size), mode="bilinear", align_corners=False + ).permute(0, 2, 3, 1) + filename = f"it{self.true_global_step}-train.png" + + def merge12(x): + return x.reshape(-1, *x.shape[2:]) + + self.save_image_grid( + filename, + [ + { + "type": "rgb", + "img": merge12(comp_rgb), + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": merge12(resize(guidance_eval_out["imgs_noisy"])), + "kwargs": {"data_format": "HWC"}, + } + ] + ) + + ( + [ + { + "type": "rgb", + "img": merge12(resize(guidance_eval_out["imgs_1step"])), + "kwargs": {"data_format": "HWC"}, + } + ] + ) + + ( + [ + { + "type": "rgb", + "img": merge12(resize(guidance_eval_out["imgs_1orig"])), + "kwargs": {"data_format": "HWC"}, + } + ] + ) + + ( + [ + { + "type": "rgb", + "img": merge12(resize(guidance_eval_out["imgs_final"])), + "kwargs": {"data_format": "HWC"}, + } + ] + ), + name="train_step", + step=self.true_global_step, + texts=guidance_eval_out["texts"], + ) diff --git a/threestudio/systems/control4d_multiview.py b/threestudio/systems/control4d_multiview.py new file mode 100644 index 0000000..8cfd9cf --- /dev/null +++ b/threestudio/systems/control4d_multiview.py @@ -0,0 +1,286 @@ +import os +from dataclasses import dataclass, field + +import torch +import torch.nn.functional as F + +import threestudio +from threestudio.systems.base import BaseLift3DSystem +from threestudio.systems.utils import parse_optimizer +from threestudio.utils.GAN.loss import discriminator_loss, generator_loss +from threestudio.utils.misc import cleanup, get_device +from threestudio.utils.ops import binary_cross_entropy, dot +from threestudio.utils.perceptual import PerceptualLoss +from threestudio.utils.typing import * + + +@threestudio.register("control4d-multiview-system") +class Control4D(BaseLift3DSystem): + @dataclass + class Config(BaseLift3DSystem.Config): + per_editing_step: int = 20 + start_editing_step: int = 2000 + + cfg: Config + + def configure(self) -> None: + # override the default configure function + self.material = threestudio.find(self.cfg.material_type)(self.cfg.material) + self.background = threestudio.find(self.cfg.background_type)( + self.cfg.background + ) + self.geometry = threestudio.find(self.cfg.geometry_type)(self.cfg.geometry) + + self.renderer = threestudio.find(self.cfg.renderer_type)( + self.cfg.renderer, + geometry=self.geometry, + material=self.material, + background=self.background, + ) + p_config = {} + self.perceptual_loss = threestudio.find("perceptual-loss")(p_config) + self.edit_frames = {} + self.per_editing_step = self.cfg.per_editing_step + self.start_editing_step = self.cfg.start_editing_step + + self.automatic_optimization = False + + def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]: + render_out = self.renderer(**batch) + return { + **render_out, + } + + def on_fit_start(self) -> None: + super().on_fit_start() + # only used in training + self.prompt_processor = threestudio.find(self.cfg.prompt_processor_type)( + self.cfg.prompt_processor + ) + self.guidance = threestudio.find(self.cfg.guidance_type)(self.cfg.guidance) + + def training_step(self, batch, batch_idx): + optimizer_g, optimizer_d = self.optimizers() + self.toggle_optimizer(optimizer_g) + + if torch.is_tensor(batch["index"]): + batch_index = batch["index"].item() + else: + batch_index = batch["index"] + batch["multi_level_guidance"] = True + + origin_gt_rgb = batch["gt_rgb"] + B, H, W, C = origin_gt_rgb.shape + if batch_index in self.edit_frames: + gt_rgb = self.edit_frames[batch_index].to(batch["gt_rgb"].device) + gt_rgb = torch.nn.functional.interpolate( + gt_rgb.permute(0, 3, 1, 2), (H, W), mode="bilinear", align_corners=False + ).permute(0, 2, 3, 1) + batch["gt_rgb"] = gt_rgb + else: + gt_rgb = origin_gt_rgb + out = self(batch) + if self.per_editing_step > 0 and self.global_step > self.start_editing_step: + prompt_utils = self.prompt_processor() + if ( + not batch_index in self.edit_frames + or self.global_step % self.per_editing_step == 0 + ): + result = self.guidance(out["comp_gan_rgb"], origin_gt_rgb, prompt_utils) + self.edit_frames[batch_index] = result["edit_images"].detach().cpu() + + loss = 0.0 + # loss of generator level 0 + loss_l1 = F.l1_loss(out["comp_int_rgb"], out["comp_gt_rgb"]) + loss_p = 0.0 + loss_kl = out["posterior"].kl().mean() + loss_G = generator_loss( + self.renderer.discriminator, + gt_rgb.permute(0, 3, 1, 2), + out["comp_gan_rgb"].permute(0, 3, 1, 2), + ) + + generator_level = out["generator_level"] + + level_ratio = 1.0 if generator_level == 2 else 0.1 + loss_l1 += F.l1_loss(out["comp_gan_rgb"], gt_rgb) * level_ratio + lr_gan_rgb = F.interpolate( + out["comp_gan_rgb"].permute(0, 3, 1, 2), (H // 4, W // 4), mode="area" + ) + lr_rgb = F.interpolate( + out["comp_rgb"].permute(0, 3, 1, 2), (H // 4, W // 4), mode="area" + ).detach() + loss_l1 += F.l1_loss(lr_gan_rgb, lr_rgb).sum() * level_ratio * 0.25 + + level_ratio = 1.0 if generator_level >= 1 else 0.1 + loss_p += ( + self.perceptual_loss( + out["comp_gan_rgb"].permute(0, 3, 1, 2).contiguous(), + gt_rgb.permute(0, 3, 1, 2).contiguous(), + ).sum() + * level_ratio + ) + + guidance_out = { + "loss_l1": loss_l1, + "loss_p": loss_p, + "loss_G": loss_G, + "loss_kl": loss_kl, + } + + for name, value in guidance_out.items(): + self.log(f"train/{name}", value) + if name.startswith("loss_"): + loss += value * self.C(self.cfg.loss[name.replace("loss_", "lambda_")]) + + if self.C(self.cfg.loss.lambda_orient) > 0: + if "normal" not in out: + raise ValueError( + "Normal is required for orientation loss, no normal is found in the output." + ) + loss_orient = ( + out["weights"].detach() + * dot(out["normal"], out["t_dirs"]).clamp_min(0.0) ** 2 + ).sum() / (out["opacity"] > 0).sum() + self.log("train/loss_orient", loss_orient) + loss += loss_orient * self.C(self.cfg.loss.lambda_orient) + + loss_sparsity = (out["opacity"] ** 2 + 0.01).sqrt().mean() + self.log("train/loss_sparsity", loss_sparsity) + loss += loss_sparsity * self.C(self.cfg.loss.lambda_sparsity) + + opacity_clamped = out["opacity"].clamp(1.0e-3, 1.0 - 1.0e-3) + loss_opaque = binary_cross_entropy(opacity_clamped, opacity_clamped) + self.log("train/loss_opaque", loss_opaque) + loss += loss_opaque * self.C(self.cfg.loss.lambda_opaque) + + for name, value in self.cfg.loss.items(): + self.log(f"train_params/{name}", self.C(value)) + + self.manual_backward(loss) + optimizer_g.step() + optimizer_g.zero_grad() + self.untoggle_optimizer(optimizer_g) + + self.toggle_optimizer(optimizer_d) + loss_D = discriminator_loss( + self.renderer.discriminator, + gt_rgb.permute(0, 3, 1, 2), + out["comp_gan_rgb"].permute(0, 3, 1, 2), + ) + loss_D *= self.C(self.cfg.loss["lambda_D"]) + self.log("train/loss_D", loss_D) + self.manual_backward(loss_D) + optimizer_d.step() + optimizer_d.zero_grad() + self.untoggle_optimizer(optimizer_d) + + def validation_step(self, batch, batch_idx): + out = self(batch) + if torch.is_tensor(batch["index"]): + batch_index = batch["index"].item() + else: + batch_index = batch["index"] + if batch_index in self.edit_frames: + B, H, W, C = batch["gt_rgb"].shape + rgb = torch.nn.functional.interpolate( + self.edit_frames[batch_index].permute(0, 3, 1, 2), (H, W) + ).permute(0, 2, 3, 1)[0] + else: + rgb = batch["gt_rgb"][0] + self.save_image_grid( + f"it{self.true_global_step}-{batch['index'][0]}.jpg", + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + [ + { + "type": "rgb", + "img": out["comp_gan_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ] + + [ + { + "type": "rgb", + "img": rgb, + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + }, + ], + name="validation_step", + step=self.true_global_step, + ) + + def on_validation_epoch_end(self): + pass + + def test_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-test/{batch['index'][0]}.png", + [ + { + "type": "rgb", + "img": out["comp_gan_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + name="test_step", + step=self.true_global_step, + ) + + def on_test_epoch_end(self): + self.save_img_sequence( + f"it{self.true_global_step}-test", + f"it{self.true_global_step}-test", + "(\d+)\.png", + save_format="mp4", + fps=30, + name="test", + step=self.true_global_step, + ) + + def configure_optimizers(self): + optimizer_g = parse_optimizer(self.cfg.optimizer, self) + optimizer_d = parse_optimizer(self.cfg.optimizer.optimizer_dis, self) + return [optimizer_g, optimizer_d], [] diff --git a/threestudio/systems/dreamfusion.py b/threestudio/systems/dreamfusion.py new file mode 100644 index 0000000..4e594b6 --- /dev/null +++ b/threestudio/systems/dreamfusion.py @@ -0,0 +1,162 @@ +from dataclasses import dataclass, field + +import torch + +import threestudio +from threestudio.systems.base import BaseLift3DSystem +from threestudio.utils.ops import binary_cross_entropy, dot +from threestudio.utils.typing import * + + +@threestudio.register("dreamfusion-system") +class DreamFusion(BaseLift3DSystem): + @dataclass + class Config(BaseLift3DSystem.Config): + pass + + cfg: Config + + def configure(self): + # create geometry, material, background, renderer + super().configure() + + def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]: + render_out = self.renderer(**batch) + return { + **render_out, + } + + def on_fit_start(self) -> None: + super().on_fit_start() + # only used in training + self.prompt_processor = threestudio.find(self.cfg.prompt_processor_type)( + self.cfg.prompt_processor + ) + self.guidance = threestudio.find(self.cfg.guidance_type)(self.cfg.guidance) + + def training_step(self, batch, batch_idx): + out = self(batch) + prompt_utils = self.prompt_processor() + guidance_out = self.guidance( + out["comp_rgb"], prompt_utils, **batch, rgb_as_latents=False + ) + + loss = 0.0 + + for name, value in guidance_out.items(): + if not (type(value) is torch.Tensor and value.numel() > 1): + self.log(f"train/{name}", value) + if name.startswith("loss_"): + loss += value * self.C(self.cfg.loss[name.replace("loss_", "lambda_")]) + + if self.C(self.cfg.loss.lambda_orient) > 0: + if "normal" not in out: + raise ValueError( + "Normal is required for orientation loss, no normal is found in the output." + ) + loss_orient = ( + out["weights"].detach() + * dot(out["normal"], out["t_dirs"]).clamp_min(0.0) ** 2 + ).sum() / (out["opacity"] > 0).sum() + self.log("train/loss_orient", loss_orient) + loss += loss_orient * self.C(self.cfg.loss.lambda_orient) + + loss_sparsity = (out["opacity"] ** 2 + 0.01).sqrt().mean() + self.log("train/loss_sparsity", loss_sparsity) + loss += loss_sparsity * self.C(self.cfg.loss.lambda_sparsity) + + opacity_clamped = out["opacity"].clamp(1.0e-3, 1.0 - 1.0e-3) + loss_opaque = binary_cross_entropy(opacity_clamped, opacity_clamped) + self.log("train/loss_opaque", loss_opaque) + loss += loss_opaque * self.C(self.cfg.loss.lambda_opaque) + + # z-variance loss proposed in HiFA: https://hifa-team.github.io/HiFA-site/ + if "z_variance" in out and "lambda_z_variance" in self.cfg.loss: + loss_z_variance = out["z_variance"][out["opacity"] > 0.5].mean() + self.log("train/loss_z_variance", loss_z_variance) + loss += loss_z_variance * self.C(self.cfg.loss.lambda_z_variance) + + for name, value in self.cfg.loss.items(): + self.log(f"train_params/{name}", self.C(value)) + + return {"loss": loss} + + def validation_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-{batch['index'][0]}.png", + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + name="validation_step", + step=self.true_global_step, + ) + + def on_validation_epoch_end(self): + pass + + def test_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-test/{batch['index'][0]}.png", + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + name="test_step", + step=self.true_global_step, + ) + + def on_test_epoch_end(self): + self.save_img_sequence( + f"it{self.true_global_step}-test", + f"it{self.true_global_step}-test", + "(\d+)\.png", + save_format="mp4", + fps=30, + name="test", + step=self.true_global_step, + ) diff --git a/threestudio/systems/eff_dreamfusion.py b/threestudio/systems/eff_dreamfusion.py new file mode 100644 index 0000000..bb9db2c --- /dev/null +++ b/threestudio/systems/eff_dreamfusion.py @@ -0,0 +1,104 @@ +from .dreamfusion import * + + +@threestudio.register("efficient-dreamfusion-system") +class EffDreamFusion(DreamFusion): + @dataclass + class Config(DreamFusion.Config): + pass + + cfg: Config + + def configure(self): + # create geometry, material, background, renderer + super().configure() + + def unmask(self, ind, subsampled_tensor, H, W): + """ + ind: B,s_H,s_W + subsampled_tensor: B,C,s_H,s_W + """ + + # Create a grid of coordinates for the original image size + offset = [ind[0, 0] % H, ind[0, 0] // H] + indices_all = torch.meshgrid( + torch.arange(W, dtype=torch.float32, device=self.device), + torch.arange(H, dtype=torch.float32, device=self.device), + indexing="xy", + ) + + grid = torch.stack( + [ + (indices_all[0] - offset[0]) * 4 / (3 * W), + (indices_all[1] - offset[1]) * 4 / (H * 3), + ], + dim=-1, + ) + grid = grid * 2 - 1 + grid = grid.repeat(subsampled_tensor.shape[0], 1, 1, 1) + # Use grid_sample to upsample the subsampled tensor (B,C,H,W) + upsampled_tensor = torch.nn.functional.grid_sample( + subsampled_tensor, grid, mode="bilinear", align_corners=True + ) + + return upsampled_tensor.permute(0, 2, 3, 1) + + def training_step(self, batch, batch_idx): + out = self(batch) + ### using mask to create image at original resolution during training + (B, s_H, s_W, C) = out["comp_rgb"].shape + comp_rgb = out["comp_rgb"].permute(0, 3, 1, 2) + mask = batch["efficiency_mask"] + comp_rgb = self.unmask(mask, comp_rgb, batch["height"], batch["width"]) + # comp_rgb = torch.zeros(B,batch["height"],batch["width"],C,device=self.device).view(B,-1,C) + # comp_rgb[:,mask.view(-1)] = out["comp_rgb"].view(B,-1,C) + out.update( + { + "comp_rgb": comp_rgb, + } + ) + + prompt_utils = self.prompt_processor() + guidance_out = self.guidance( + out["comp_rgb"], prompt_utils, **batch, rgb_as_latents=False + ) + + loss = 0.0 + + for name, value in guidance_out.items(): + if not (type(value) is torch.Tensor and value.numel() > 1): + self.log(f"train/{name}", value) + if name.startswith("loss_"): + loss += value * self.C(self.cfg.loss[name.replace("loss_", "lambda_")]) + + if self.C(self.cfg.loss.lambda_orient) > 0: + if "normal" not in out: + raise ValueError( + "Normal is required for orientation loss, no normal is found in the output." + ) + loss_orient = ( + out["weights"].detach() + * dot(out["normal"], out["t_dirs"]).clamp_min(0.0) ** 2 + ).sum() / (out["opacity"] > 0).sum() + self.log("train/loss_orient", loss_orient) + loss += loss_orient * self.C(self.cfg.loss.lambda_orient) + + loss_sparsity = (out["opacity"] ** 2 + 0.01).sqrt().mean() + self.log("train/loss_sparsity", loss_sparsity) + loss += loss_sparsity * self.C(self.cfg.loss.lambda_sparsity) + + opacity_clamped = out["opacity"].clamp(1.0e-3, 1.0 - 1.0e-3) + loss_opaque = binary_cross_entropy(opacity_clamped, opacity_clamped) + self.log("train/loss_opaque", loss_opaque) + loss += loss_opaque * self.C(self.cfg.loss.lambda_opaque) + + # z-variance loss proposed in HiFA: https://hifa-team.github.io/HiFA-site/ + if "z_variance" in out and "lambda_z_variance" in self.cfg.loss: + loss_z_variance = out["z_variance"][out["opacity"] > 0.5].mean() + self.log("train/loss_z_variance", loss_z_variance) + loss += loss_z_variance * self.C(self.cfg.loss.lambda_z_variance) + + for name, value in self.cfg.loss.items(): + self.log(f"train_params/{name}", self.C(value)) + + return {"loss": loss} diff --git a/threestudio/systems/fantasia3d.py b/threestudio/systems/fantasia3d.py new file mode 100644 index 0000000..26d8d63 --- /dev/null +++ b/threestudio/systems/fantasia3d.py @@ -0,0 +1,168 @@ +from dataclasses import dataclass, field + +import torch +import torch.nn.functional as F + +import threestudio +from threestudio.systems.base import BaseLift3DSystem +from threestudio.utils.ops import binary_cross_entropy, dot +from threestudio.utils.typing import * + + +@threestudio.register("fantasia3d-system") +class Fantasia3D(BaseLift3DSystem): + @dataclass + class Config(BaseLift3DSystem.Config): + latent_steps: int = 1000 + texture: bool = False + + cfg: Config + + def configure(self): + # create geometry, material, background, renderer + super().configure() + + def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]: + render_out = self.renderer(**batch, render_rgb=self.cfg.texture) + return { + **render_out, + } + + def on_fit_start(self) -> None: + super().on_fit_start() + # only used in training + self.prompt_processor = threestudio.find(self.cfg.prompt_processor_type)( + self.cfg.prompt_processor + ) + self.guidance = threestudio.find(self.cfg.guidance_type)(self.cfg.guidance) + + if not self.cfg.texture: + # initialize SDF + # FIXME: what if using other geometry types? + self.geometry.initialize_shape() + + def training_step(self, batch, batch_idx): + loss = 0.0 + + out = self(batch) + prompt_utils = self.prompt_processor() + + if not self.cfg.texture: # geometry training + if self.true_global_step < self.cfg.latent_steps: + guidance_inp = torch.cat( + [out["comp_normal"] * 2.0 - 1.0, out["opacity"]], dim=-1 + ) + guidance_out = self.guidance( + guidance_inp, prompt_utils, **batch, rgb_as_latents=True + ) + else: + guidance_inp = out["comp_normal"] + guidance_out = self.guidance( + guidance_inp, prompt_utils, **batch, rgb_as_latents=False + ) + + loss_normal_consistency = out["mesh"].normal_consistency() + self.log("train/loss_normal_consistency", loss_normal_consistency) + loss += loss_normal_consistency * self.C( + self.cfg.loss.lambda_normal_consistency + ) + else: # texture training + guidance_inp = out["comp_rgb"] + if isinstance( + self.guidance, + threestudio.models.guidance.controlnet_guidance.ControlNetGuidance, + ): + cond_inp = out["comp_normal"] + guidance_out = self.guidance( + guidance_inp, cond_inp, prompt_utils, **batch, rgb_as_latents=False + ) + else: + guidance_out = self.guidance( + guidance_inp, prompt_utils, **batch, rgb_as_latents=False + ) + + for name, value in guidance_out.items(): + self.log(f"train/{name}", value) + if name.startswith("loss_"): + loss += value * self.C(self.cfg.loss[name.replace("loss_", "lambda_")]) + + for name, value in self.cfg.loss.items(): + self.log(f"train_params/{name}", self.C(value)) + + return {"loss": loss} + + def validation_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-{batch['index'][0]}.png", + ( + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if self.cfg.texture + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + }, + ], + name="validation_step", + step=self.true_global_step, + ) + + def on_validation_epoch_end(self): + pass + + def test_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-test/{batch['index'][0]}.png", + ( + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if self.cfg.texture + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + }, + ], + name="test_step", + step=self.true_global_step, + ) + + def on_test_epoch_end(self): + self.save_img_sequence( + f"it{self.true_global_step}-test", + f"it{self.true_global_step}-test", + "(\d+)\.png", + save_format="mp4", + fps=30, + name="test", + step=self.true_global_step, + ) diff --git a/threestudio/systems/imagedreamfusion.py b/threestudio/systems/imagedreamfusion.py new file mode 100644 index 0000000..63108a3 --- /dev/null +++ b/threestudio/systems/imagedreamfusion.py @@ -0,0 +1,387 @@ +import os +import random +import shutil +from dataclasses import dataclass, field + +import torch +import torch.nn.functional as F +from torchmetrics import PearsonCorrCoef + +import threestudio +from threestudio.systems.base import BaseLift3DSystem +from threestudio.utils.ops import binary_cross_entropy, dot +from threestudio.utils.typing import * + + +@threestudio.register("image-condition-dreamfusion-system") +class ImageConditionDreamFusion(BaseLift3DSystem): + @dataclass + class Config(BaseLift3DSystem.Config): + freq: dict = field(default_factory=dict) + refinement: bool = False + ambient_ratio_min: float = 0.5 + + cfg: Config + + def configure(self): + # create geometry, material, background, renderer + super().configure() + + def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]: + render_out = self.renderer(**batch) + return { + **render_out, + } + + def on_fit_start(self) -> None: + super().on_fit_start() + # only used in training + self.prompt_processor = threestudio.find(self.cfg.prompt_processor_type)( + self.cfg.prompt_processor + ) + self.guidance = threestudio.find(self.cfg.guidance_type)(self.cfg.guidance) + + # visualize all training images + all_images = self.trainer.datamodule.train_dataloader().dataset.get_all_images() + self.save_image_grid( + "all_training_images.png", + [ + {"type": "rgb", "img": image, "kwargs": {"data_format": "HWC"}} + for image in all_images + ], + name="on_fit_start", + step=self.true_global_step, + ) + + self.pearson = PearsonCorrCoef().to(self.device) + + def training_substep(self, batch, batch_idx, guidance: str): + """ + Args: + guidance: one of "ref" (reference image supervision), "guidance" + """ + if guidance == "ref": + # bg_color = torch.rand_like(batch['rays_o']) + ambient_ratio = 1.0 + shading = "diffuse" + batch["shading"] = shading + elif guidance == "guidance": + batch = batch["random_camera"] + ambient_ratio = ( + self.cfg.ambient_ratio_min + + (1 - self.cfg.ambient_ratio_min) * random.random() + ) + + batch["bg_color"] = None + batch["ambient_ratio"] = ambient_ratio + + out = self(batch) + loss_prefix = f"loss_{guidance}_" + + loss_terms = {} + + def set_loss(name, value): + loss_terms[f"{loss_prefix}{name}"] = value + + guidance_eval = ( + guidance == "guidance" + and self.cfg.freq.guidance_eval > 0 + and self.true_global_step % self.cfg.freq.guidance_eval == 0 + ) + + if guidance == "ref": + gt_mask = batch["mask"] + gt_rgb = batch["rgb"] + + # color loss + gt_rgb = gt_rgb * gt_mask.float() + out["comp_rgb_bg"] * ( + 1 - gt_mask.float() + ) + set_loss("rgb", F.mse_loss(gt_rgb, out["comp_rgb"])) + + # mask loss + set_loss("mask", F.mse_loss(gt_mask.float(), out["opacity"])) + + # depth loss + if self.C(self.cfg.loss.lambda_depth) > 0: + valid_gt_depth = batch["ref_depth"][gt_mask.squeeze(-1)].unsqueeze(1) + valid_pred_depth = out["depth"][gt_mask].unsqueeze(1) + with torch.no_grad(): + A = torch.cat( + [valid_gt_depth, torch.ones_like(valid_gt_depth)], dim=-1 + ) # [B, 2] + X = torch.linalg.lstsq(A, valid_pred_depth).solution # [2, 1] + valid_gt_depth = A @ X # [B, 1] + set_loss("depth", F.mse_loss(valid_gt_depth, valid_pred_depth)) + + # relative depth loss + if self.C(self.cfg.loss.lambda_depth_rel) > 0: + valid_gt_depth = batch["ref_depth"][gt_mask.squeeze(-1)] # [B,] + valid_pred_depth = out["depth"][gt_mask] # [B,] + set_loss( + "depth_rel", 1 - self.pearson(valid_pred_depth, valid_gt_depth) + ) + + # normal loss + if self.C(self.cfg.loss.lambda_normal) > 0: + valid_gt_normal = ( + 1 - 2 * batch["ref_normal"][gt_mask.squeeze(-1)] + ) # [B, 3] + valid_pred_normal = ( + 2 * out["comp_normal"][gt_mask.squeeze(-1)] - 1 + ) # [B, 3] + set_loss( + "normal", + 1 - F.cosine_similarity(valid_pred_normal, valid_gt_normal).mean(), + ) + elif guidance == "guidance": + self.guidance.set_min_max_steps( + self.C(self.guidance.cfg.min_step_percent), + self.C(self.guidance.cfg.max_step_percent), + ) + prompt_utils = self.prompt_processor() + guidance_out = self.guidance( + out["comp_rgb"], + prompt_utils, + **batch, + rgb_as_latents=False, + guidance_eval=guidance_eval, + ) + set_loss("sds", guidance_out["loss_sds"]) + + if self.C(self.cfg.loss.lambda_normal_smooth) > 0: + if "comp_normal" not in out: + raise ValueError( + "comp_normal is required for 2D normal smooth loss, no comp_normal is found in the output." + ) + normal = out["comp_normal"] + set_loss( + "normal_smooth", + (normal[:, 1:, :, :] - normal[:, :-1, :, :]).square().mean() + + (normal[:, :, 1:, :] - normal[:, :, :-1, :]).square().mean(), + ) + + if self.C(self.cfg.loss.lambda_3d_normal_smooth) > 0: + if "normal" not in out: + raise ValueError( + "Normal is required for normal smooth loss, no normal is found in the output." + ) + if "normal_perturb" not in out: + raise ValueError( + "normal_perturb is required for normal smooth loss, no normal_perturb is found in the output." + ) + normals = out["normal"] + normals_perturb = out["normal_perturb"] + set_loss("3d_normal_smooth", (normals - normals_perturb).abs().mean()) + + if not self.cfg.refinement: + if self.C(self.cfg.loss.lambda_orient) > 0: + if "normal" not in out: + raise ValueError( + "Normal is required for orientation loss, no normal is found in the output." + ) + set_loss( + "orient", + ( + out["weights"].detach() + * dot(out["normal"], out["t_dirs"]).clamp_min(0.0) ** 2 + ).sum() + / (out["opacity"] > 0).sum(), + ) + + if guidance != "ref" and self.C(self.cfg.loss.lambda_sparsity) > 0: + set_loss("sparsity", (out["opacity"] ** 2 + 0.01).sqrt().mean()) + + if self.C(self.cfg.loss.lambda_opaque) > 0: + opacity_clamped = out["opacity"].clamp(1.0e-3, 1.0 - 1.0e-3) + set_loss( + "opaque", binary_cross_entropy(opacity_clamped, opacity_clamped) + ) + else: + if self.C(self.cfg.loss.lambda_normal_consistency) > 0: + set_loss("normal_consistency", out["mesh"].normal_consistency()) + if self.C(self.cfg.loss.lambda_laplacian_smoothness) > 0: + set_loss("laplacian_smoothness", out["mesh"].laplacian()) + + loss = 0.0 + for name, value in loss_terms.items(): + self.log(f"train/{name}", value) + if name.startswith(loss_prefix): + loss_weighted = value * self.C( + self.cfg.loss[name.replace(loss_prefix, "lambda_")] + ) + self.log(f"train/{name}_w", loss_weighted) + loss += loss_weighted + + for name, value in self.cfg.loss.items(): + self.log(f"train_params/{name}", self.C(value)) + + self.log(f"train/loss_{guidance}", loss) + + if guidance_eval: + self.guidance_evaluation_save( + out["comp_rgb"].detach()[: guidance_out["eval"]["bs"]], + guidance_out["eval"], + ) + + return {"loss": loss} + + def training_step(self, batch, batch_idx): + total_loss = 0.0 + + # guidance + if self.true_global_step > self.cfg.freq.ref_only_steps: + out = self.training_substep(batch, batch_idx, guidance="guidance") + total_loss += out["loss"] + + # ref + out = self.training_substep(batch, batch_idx, guidance="ref") + total_loss += out["loss"] + + self.log("train/loss", total_loss, prog_bar=True) + + # sch = self.lr_schedulers() + # sch.step() + + return {"loss": total_loss} + + def validation_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-val/{batch['index'][0]}.png", + ( + [ + { + "type": "rgb", + "img": batch["rgb"][0], + "kwargs": {"data_format": "HWC"}, + } + ] + if "rgb" in batch + else [] + ) + + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + ( + [ + { + "type": "grayscale", + "img": out["depth"][0], + "kwargs": {}, + } + ] + if "depth" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + name=f"validation_step_batchidx_{batch_idx}" + if batch_idx in [0, 7, 15, 23, 29] + else None, + step=self.true_global_step, + ) + + def on_validation_epoch_end(self): + filestem = f"it{self.true_global_step}-val" + self.save_img_sequence( + filestem, + filestem, + "(\d+)\.png", + save_format="mp4", + fps=30, + name="validation_epoch_end", + step=self.true_global_step, + ) + shutil.rmtree( + os.path.join(self.get_save_dir(), f"it{self.true_global_step}-val") + ) + + def test_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-test/{batch['index'][0]}.png", + ( + [ + { + "type": "rgb", + "img": batch["rgb"][0], + "kwargs": {"data_format": "HWC"}, + } + ] + if "rgb" in batch + else [] + ) + + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + ( + [ + { + "type": "grayscale", + "img": out["depth"][0], + "kwargs": {}, + } + ] + if "depth" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + name="test_step", + step=self.true_global_step, + ) + + def on_test_epoch_end(self): + self.save_img_sequence( + f"it{self.true_global_step}-test", + f"it{self.true_global_step}-test", + "(\d+)\.png", + save_format="mp4", + fps=30, + name="test", + step=self.true_global_step, + ) + shutil.rmtree( + os.path.join(self.get_save_dir(), f"it{self.true_global_step}-test") + ) diff --git a/threestudio/systems/instructnerf2nerf.py b/threestudio/systems/instructnerf2nerf.py new file mode 100644 index 0000000..f6e3ecd --- /dev/null +++ b/threestudio/systems/instructnerf2nerf.py @@ -0,0 +1,213 @@ +import os +from dataclasses import dataclass, field + +import torch + +import threestudio +from threestudio.systems.base import BaseLift3DSystem +from threestudio.utils.misc import cleanup, get_device +from threestudio.utils.ops import binary_cross_entropy, dot +from threestudio.utils.perceptual import PerceptualLoss +from threestudio.utils.typing import * + + +@threestudio.register("instructnerf2nerf-system") +class Instructnerf2nerf(BaseLift3DSystem): + @dataclass + class Config(BaseLift3DSystem.Config): + per_editing_step: int = 10 + start_editing_step: int = 1000 + + cfg: Config + + def configure(self): + # create geometry, material, background, renderer + super().configure() + self.edit_frames = {} + p_config = {} + self.perceptual_loss = threestudio.find("perceptual-loss")(p_config) + + def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]: + render_out = self.renderer(**batch) + return { + **render_out, + } + + def on_fit_start(self) -> None: + super().on_fit_start() + # only used in training + self.prompt_processor = threestudio.find(self.cfg.prompt_processor_type)( + self.cfg.prompt_processor + ) + self.guidance = threestudio.find(self.cfg.guidance_type)(self.cfg.guidance) + + def training_step(self, batch, batch_idx): + if torch.is_tensor(batch["index"]): + batch_index = batch["index"].item() + else: + batch_index = batch["index"] + origin_gt_rgb = batch["gt_rgb"] + B, H, W, C = origin_gt_rgb.shape + if batch_index in self.edit_frames: + gt_rgb = self.edit_frames[batch_index].to(batch["gt_rgb"].device) + gt_rgb = torch.nn.functional.interpolate( + gt_rgb.permute(0, 3, 1, 2), (H, W), mode="bilinear", align_corners=False + ).permute(0, 2, 3, 1) + batch["gt_rgb"] = gt_rgb + else: + gt_rgb = origin_gt_rgb + out = self(batch) + if ( + self.cfg.per_editing_step > 0 + and self.global_step > self.cfg.start_editing_step + ): + prompt_utils = self.prompt_processor() + if ( + not batch_index in self.edit_frames + or self.global_step % self.cfg.per_editing_step == 0 + ): + self.renderer.eval() + full_out = self(batch) + self.renderer.train() + result = self.guidance( + full_out["comp_rgb"], origin_gt_rgb, prompt_utils + ) + self.edit_frames[batch_index] = result["edit_images"].detach().cpu() + + loss = 0.0 + guidance_out = { + "loss_l1": torch.nn.functional.l1_loss(out["comp_rgb"], gt_rgb), + "loss_p": self.perceptual_loss( + out["comp_rgb"].permute(0, 3, 1, 2).contiguous(), + gt_rgb.permute(0, 3, 1, 2).contiguous(), + ).sum(), + } + + for name, value in guidance_out.items(): + self.log(f"train/{name}", value) + if name.startswith("loss_"): + loss += value * self.C(self.cfg.loss[name.replace("loss_", "lambda_")]) + + if self.C(self.cfg.loss.lambda_orient) > 0: + if "normal" not in out: + raise ValueError( + "Normal is required for orientation loss, no normal is found in the output." + ) + loss_orient = ( + out["weights"].detach() + * dot(out["normal"], out["t_dirs"]).clamp_min(0.0) ** 2 + ).sum() / (out["opacity"] > 0).sum() + self.log("train/loss_orient", loss_orient) + loss += loss_orient * self.C(self.cfg.loss.lambda_orient) + + loss_sparsity = (out["opacity"] ** 2 + 0.01).sqrt().mean() + self.log("train/loss_sparsity", loss_sparsity) + loss += loss_sparsity * self.C(self.cfg.loss.lambda_sparsity) + + opacity_clamped = out["opacity"].clamp(1.0e-3, 1.0 - 1.0e-3) + loss_opaque = binary_cross_entropy(opacity_clamped, opacity_clamped) + self.log("train/loss_opaque", loss_opaque) + loss += loss_opaque * self.C(self.cfg.loss.lambda_opaque) + + for name, value in self.cfg.loss.items(): + self.log(f"train_params/{name}", self.C(value)) + + return {"loss": loss} + + def validation_step(self, batch, batch_idx): + out = self(batch) + if torch.is_tensor(batch["index"]): + batch_index = batch["index"].item() + else: + batch_index = batch["index"] + if batch_index in self.edit_frames: + B, H, W, C = batch["gt_rgb"].shape + rgb = torch.nn.functional.interpolate( + self.edit_frames[batch_index].permute(0, 3, 1, 2), (H, W) + ).permute(0, 2, 3, 1)[0] + else: + rgb = batch["gt_rgb"][0] + self.save_image_grid( + f"it{self.true_global_step}-{batch['index'][0]}.png", + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ] + + [ + { + "type": "rgb", + "img": rgb, + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + }, + ], + name="validation_step", + step=self.true_global_step, + ) + + def on_validation_epoch_end(self): + pass + + def test_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-test/{batch['index'][0]}.png", + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + name="test_step", + step=self.true_global_step, + ) + + def on_test_epoch_end(self): + self.save_img_sequence( + f"it{self.true_global_step}-test", + f"it{self.true_global_step}-test", + "(\d+)\.png", + save_format="mp4", + fps=30, + name="test", + step=self.true_global_step, + ) diff --git a/threestudio/systems/latentnerf.py b/threestudio/systems/latentnerf.py new file mode 100644 index 0000000..48804c2 --- /dev/null +++ b/threestudio/systems/latentnerf.py @@ -0,0 +1,181 @@ +from dataclasses import dataclass, field + +import torch + +import threestudio +from threestudio.systems.base import BaseLift3DSystem +from threestudio.utils.ops import ShapeLoss, binary_cross_entropy, dot +from threestudio.utils.typing import * + + +@threestudio.register("latentnerf-system") +class LatentNeRF(BaseLift3DSystem): + @dataclass + class Config(BaseLift3DSystem.Config): + guide_shape: Optional[str] = None + refinement: bool = False + + cfg: Config + + def configure(self): + # create geometry, material, background, renderer + super().configure() + + if self.training or not self.cfg.refinement: + self.guidance = threestudio.find(self.cfg.guidance_type)(self.cfg.guidance) + + if self.cfg.guide_shape is not None: + self.shape_loss = ShapeLoss(self.cfg.guide_shape) + + def forward(self, batch: Dict[str, Any], decode: bool = False) -> Dict[str, Any]: + render_out = self.renderer(**batch) + out = { + **render_out, + } + if decode: + if self.cfg.refinement: + out["decoded_rgb"] = out["comp_rgb"] + else: + out["decoded_rgb"] = self.guidance.decode_latents( + out["comp_rgb"].permute(0, 3, 1, 2) + ).permute(0, 2, 3, 1) + return out + + def on_fit_start(self) -> None: + super().on_fit_start() + # only used in training + self.prompt_processor = threestudio.find(self.cfg.prompt_processor_type)( + self.cfg.prompt_processor + ) + + def training_step(self, batch, batch_idx): + out = self(batch) + prompt_utils = self.prompt_processor() + guidance_out = self.guidance( + out["comp_rgb"], + prompt_utils, + **batch, + rgb_as_latents=not self.cfg.refinement, + ) + + loss = 0.0 + + for name, value in guidance_out.items(): + self.log(f"train/{name}", value) + if name.startswith("loss_"): + loss += value * self.C(self.cfg.loss[name.replace("loss_", "lambda_")]) + + if self.C(self.cfg.loss.lambda_orient) > 0: + if "normal" not in out: + raise ValueError( + "Normal is required for orientation loss, no normal is found in the output." + ) + loss_orient = ( + out["weights"].detach() + * dot(out["normal"], out["t_dirs"]).clamp_min(0.0) ** 2 + ).sum() / (out["opacity"] > 0).sum() + self.log("train/loss_orient", loss_orient) + loss += loss_orient * self.C(self.cfg.loss.lambda_orient) + + loss_sparsity = (out["opacity"] ** 2 + 0.01).sqrt().mean() + self.log("train/loss_sparsity", loss_sparsity) + loss += loss_sparsity * self.C(self.cfg.loss.lambda_sparsity) + + opacity_clamped = out["opacity"].clamp(1.0e-3, 1.0 - 1.0e-3) + loss_opaque = binary_cross_entropy(opacity_clamped, opacity_clamped) + self.log("train/loss_opaque", loss_opaque) + loss += loss_opaque * self.C(self.cfg.loss.lambda_opaque) + + if ( + self.cfg.guide_shape is not None + and self.C(self.cfg.loss.lambda_shape) > 0 + and out["points"].shape[0] > 0 + ): + loss_shape = self.shape_loss(out["points"], out["density"]) + self.log("train/loss_shape", loss_shape) + loss += loss_shape * self.C(self.cfg.loss.lambda_shape) + + for name, value in self.cfg.loss.items(): + self.log(f"train_params/{name}", self.C(value)) + + return {"loss": loss} + + def validation_step(self, batch, batch_idx): + out = self(batch, decode=True) + self.save_image_grid( + f"it{self.true_global_step}-{batch['index'][0]}.png", + [ + { + "type": "rgb", + "img": out["decoded_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + name="validation_step", + step=self.true_global_step, + ) + + def on_validation_epoch_end(self): + pass + + def test_step(self, batch, batch_idx): + out = self(batch, decode=True) + self.save_image_grid( + f"it{self.true_global_step}-test/{batch['index'][0]}.png", + [ + { + "type": "rgb", + "img": out["decoded_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + name="test_step", + step=self.true_global_step, + ) + + def on_test_epoch_end(self): + self.save_img_sequence( + f"it{self.true_global_step}-test", + f"it{self.true_global_step}-test", + "(\d+)\.png", + save_format="mp4", + fps=30, + name="test", + step=self.true_global_step, + ) diff --git a/threestudio/systems/magic123.py b/threestudio/systems/magic123.py new file mode 100644 index 0000000..1255151 --- /dev/null +++ b/threestudio/systems/magic123.py @@ -0,0 +1,230 @@ +from dataclasses import dataclass, field + +import torch +import torch.nn.functional as F + +import threestudio +from threestudio.systems.base import BaseLift3DSystem +from threestudio.utils.ops import binary_cross_entropy, dot +from threestudio.utils.typing import * + + +@threestudio.register("magic123-system") +class Magic123(BaseLift3DSystem): + @dataclass + class Config(BaseLift3DSystem.Config): + refinement: bool = False + guidance_3d_type: str = "" + guidance_3d: dict = field(default_factory=dict) + + cfg: Config + + def configure(self): + # create geometry, material, background, renderer + super().configure() + self.guidance = threestudio.find(self.cfg.guidance_type)(self.cfg.guidance) + self.guidance_3d = threestudio.find(self.cfg.guidance_3d_type)( + self.cfg.guidance_3d + ) + + def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]: + render_out = self.renderer(**batch) + return { + **render_out, + } + + def on_fit_start(self) -> None: + super().on_fit_start() + self.prompt_processor = threestudio.find(self.cfg.prompt_processor_type)( + self.cfg.prompt_processor + ) + + def training_step(self, batch, batch_idx): + out_input = self(batch) + out = self(batch["random_camera"]) + prompt_utils = self.prompt_processor() + guidance_out = self.guidance( + out["comp_rgb"], + prompt_utils, + **batch["random_camera"], + rgb_as_latents=False, + ) + guidance_3d_out = self.guidance_3d( + out["comp_rgb"], + **batch["random_camera"], + rgb_as_latents=False, + ) + + loss = 0.0 + + loss_rgb = F.mse_loss( + out_input["comp_rgb"], + batch["rgb"] * batch["mask"].float() + + out_input["comp_rgb_bg"] * (1.0 - batch["mask"].float()), + ) + self.log("train/loss_rgb", loss_rgb) + loss += loss_rgb * self.C(self.cfg.loss.lambda_rgb) + + loss_mask = F.binary_cross_entropy( + out_input["opacity"].clamp(1.0e-5, 1.0 - 1.0e-5), + batch["mask"].float(), + ) + self.log("train/loss_mask", loss_mask) + loss += loss_mask * self.C(self.cfg.loss.lambda_mask) + + for name, value in guidance_out.items(): + if not (isinstance(value, torch.Tensor) and len(value.shape) > 0): + self.log(f"train/{name}", value) + if name.startswith("loss_"): + loss += value * self.C(self.cfg.loss[name.replace("loss_", "lambda_")]) + + for name, value in guidance_3d_out.items(): + if not (isinstance(value, torch.Tensor) and len(value.shape) > 0): + self.log(f"train/{name}_3d", value) + if name.startswith("loss_"): + loss += value * self.C( + self.cfg.loss[name.replace("loss_", "lambda_3d_")] + ) + + if not self.cfg.refinement: + if self.C(self.cfg.loss.lambda_orient) > 0: + if "normal" not in out: + raise ValueError( + "Normal is required for orientation loss, no normal is found in the output." + ) + loss_orient = ( + out["weights"].detach() + * dot(out["normal"], out["t_dirs"]).clamp_min(0.0) ** 2 + ).sum() / (out["opacity"] > 0).sum() + self.log("train/loss_orient", loss_orient) + loss += loss_orient * self.C(self.cfg.loss.lambda_orient) + + if self.C(self.cfg.loss.lambda_normal_smoothness_2d) > 0: + if "comp_normal" not in out: + raise ValueError( + "comp_normal is required for 2D normal smoothness loss, no comp_normal is found in the output." + ) + normal = out["comp_normal"] + loss_normal_smoothness_2d = ( + normal[:, 1:, :, :] - normal[:, :-1, :, :] + ).square().mean() + ( + normal[:, :, 1:, :] - normal[:, :, :-1, :] + ).square().mean() + self.log("trian/loss_normal_smoothness_2d", loss_normal_smoothness_2d) + loss += loss_normal_smoothness_2d * self.C( + self.cfg.loss.lambda_normal_smoothness_2d + ) + + loss_sparsity = (out["opacity"] ** 2 + 0.01).sqrt().mean() + self.log("train/loss_sparsity", loss_sparsity) + loss += loss_sparsity * self.C(self.cfg.loss.lambda_sparsity) + + opacity_clamped = out["opacity"].clamp(1.0e-3, 1.0 - 1.0e-3) + loss_opaque = binary_cross_entropy(opacity_clamped, opacity_clamped) + self.log("train/loss_opaque", loss_opaque) + loss += loss_opaque * self.C(self.cfg.loss.lambda_opaque) + + # z variance loss proposed in HiFA: http://arxiv.org/abs/2305.18766 + # helps reduce floaters and produce solid geometry + if "z_variance" in out and "lambda_z_variance" in self.cfg.loss: + loss_z_variance = out["z_variance"][out["opacity"] > 0.5].mean() + self.log("train/loss_z_variance", loss_z_variance) + loss += loss_z_variance * self.C(self.cfg.loss.lambda_z_variance) + else: + loss_normal_consistency = out["mesh"].normal_consistency() + self.log("train/loss_normal_consistency", loss_normal_consistency) + loss += loss_normal_consistency * self.C( + self.cfg.loss.lambda_normal_consistency + ) + + if self.C(self.cfg.loss.lambda_laplacian_smoothness) > 0: + loss_laplacian_smoothness = out["mesh"].laplacian() + self.log("train/loss_laplacian_smoothness", loss_laplacian_smoothness) + loss += loss_laplacian_smoothness * self.C( + self.cfg.loss.lambda_laplacian_smoothness + ) + + for name, value in self.cfg.loss.items(): + self.log(f"train_params/{name}", self.C(value)) + + return {"loss": loss} + + def validation_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-{batch['index'][0]}.png", + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + name="validation_step", + step=self.true_global_step, + ) + + def on_validation_epoch_end(self): + pass + + def test_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-test/{batch['index'][0]}.png", + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + name="test_step", + step=self.true_global_step, + ) + + def on_test_epoch_end(self): + self.save_img_sequence( + f"it{self.true_global_step}-test", + f"it{self.true_global_step}-test", + "(\d+)\.png", + save_format="mp4", + fps=30, + name="test", + step=self.true_global_step, + ) diff --git a/threestudio/systems/magic3d.py b/threestudio/systems/magic3d.py new file mode 100644 index 0000000..9b1666a --- /dev/null +++ b/threestudio/systems/magic3d.py @@ -0,0 +1,164 @@ +import os +from dataclasses import dataclass, field + +import torch + +import threestudio +from threestudio.systems.base import BaseLift3DSystem +from threestudio.utils.misc import cleanup, get_device +from threestudio.utils.ops import binary_cross_entropy, dot +from threestudio.utils.typing import * + + +@threestudio.register("magic3d-system") +class Magic3D(BaseLift3DSystem): + @dataclass + class Config(BaseLift3DSystem.Config): + refinement: bool = False + + cfg: Config + + def configure(self): + # create geometry, material, background, renderer + super().configure() + + def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]: + render_out = self.renderer(**batch) + return { + **render_out, + } + + def on_fit_start(self) -> None: + super().on_fit_start() + # only used in training + self.prompt_processor = threestudio.find(self.cfg.prompt_processor_type)( + self.cfg.prompt_processor + ) + self.guidance = threestudio.find(self.cfg.guidance_type)(self.cfg.guidance) + + def training_step(self, batch, batch_idx): + out = self(batch) + prompt_utils = self.prompt_processor() + guidance_out = self.guidance( + out["comp_rgb"], prompt_utils, **batch, rgb_as_latents=False + ) + + loss = 0.0 + + for name, value in guidance_out.items(): + self.log(f"train/{name}", value) + if name.startswith("loss_"): + loss += value * self.C(self.cfg.loss[name.replace("loss_", "lambda_")]) + + if not self.cfg.refinement: + if self.C(self.cfg.loss.lambda_orient) > 0: + if "normal" not in out: + raise ValueError( + "Normal is required for orientation loss, no normal is found in the output." + ) + loss_orient = ( + out["weights"].detach() + * dot(out["normal"], out["t_dirs"]).clamp_min(0.0) ** 2 + ).sum() / (out["opacity"] > 0).sum() + self.log("train/loss_orient", loss_orient) + loss += loss_orient * self.C(self.cfg.loss.lambda_orient) + + loss_sparsity = (out["opacity"] ** 2 + 0.01).sqrt().mean() + self.log("train/loss_sparsity", loss_sparsity) + loss += loss_sparsity * self.C(self.cfg.loss.lambda_sparsity) + + opacity_clamped = out["opacity"].clamp(1.0e-3, 1.0 - 1.0e-3) + loss_opaque = binary_cross_entropy(opacity_clamped, opacity_clamped) + self.log("train/loss_opaque", loss_opaque) + loss += loss_opaque * self.C(self.cfg.loss.lambda_opaque) + else: + loss_normal_consistency = out["mesh"].normal_consistency() + self.log("train/loss_normal_consistency", loss_normal_consistency) + loss += loss_normal_consistency * self.C( + self.cfg.loss.lambda_normal_consistency + ) + + for name, value in self.cfg.loss.items(): + self.log(f"train_params/{name}", self.C(value)) + + return {"loss": loss} + + def validation_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-{batch['index'][0]}.png", + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + name="validation_step", + step=self.true_global_step, + ) + + def on_validation_epoch_end(self): + pass + + def test_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-test/{batch['index'][0]}.png", + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + name="test_step", + step=self.true_global_step, + ) + + def on_test_epoch_end(self): + self.save_img_sequence( + f"it{self.true_global_step}-test", + f"it{self.true_global_step}-test", + "(\d+)\.png", + save_format="mp4", + fps=30, + name="test", + step=self.true_global_step, + ) diff --git a/threestudio/systems/optimizers.py b/threestudio/systems/optimizers.py new file mode 100644 index 0000000..cfc426e --- /dev/null +++ b/threestudio/systems/optimizers.py @@ -0,0 +1,315 @@ +# Copyright 2022 Garena Online Private Limited +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from typing import List + +import torch +from torch import Tensor +from torch.optim.optimizer import Optimizer + + +class Adan(Optimizer): + """ + Implements a pytorch variant of Adan + Adan was proposed in + Adan: Adaptive Nesterov Momentum Algorithm for + Faster Optimizing Deep Models[J].arXiv preprint arXiv:2208.06677, 2022. + https://arxiv.org/abs/2208.06677 + Arguments: + params (iterable): iterable of parameters to optimize or + dicts defining parameter groups. + lr (float, optional): learning rate. (default: 1e-3) + betas (Tuple[float, float, flot], optional): coefficients used for + first- and second-order moments. (default: (0.98, 0.92, 0.99)) + eps (float, optional): term added to the denominator to improve + numerical stability. (default: 1e-8) + weight_decay (float, optional): decoupled weight decay + (L2 penalty) (default: 0) + max_grad_norm (float, optional): value used to clip + global grad norm (default: 0.0 no clip) + no_prox (bool): how to perform the decoupled weight decay + (default: False) + foreach (bool): if True would use torch._foreach implementation. + It's faster but uses slightly more memory. (default: True) + """ + + def __init__( + self, + params, + lr=1e-3, + betas=(0.98, 0.92, 0.99), + eps=1e-8, + weight_decay=0.0, + max_grad_norm=0.0, + no_prox=False, + foreach: bool = True, + ): + if not 0.0 <= max_grad_norm: + raise ValueError("Invalid Max grad norm: {}".format(max_grad_norm)) + if not 0.0 <= lr: + raise ValueError("Invalid learning rate: {}".format(lr)) + if not 0.0 <= eps: + raise ValueError("Invalid epsilon value: {}".format(eps)) + if not 0.0 <= betas[0] < 1.0: + raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) + if not 0.0 <= betas[1] < 1.0: + raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) + if not 0.0 <= betas[2] < 1.0: + raise ValueError("Invalid beta parameter at index 2: {}".format(betas[2])) + defaults = dict( + lr=lr, + betas=betas, + eps=eps, + weight_decay=weight_decay, + max_grad_norm=max_grad_norm, + no_prox=no_prox, + foreach=foreach, + ) + super().__init__(params, defaults) + + def __setstate__(self, state): + super(Adan, self).__setstate__(state) + for group in self.param_groups: + group.setdefault("no_prox", False) + + @torch.no_grad() + def restart_opt(self): + for group in self.param_groups: + group["step"] = 0 + for p in group["params"]: + if p.requires_grad: + state = self.state[p] + # State initialization + + # Exponential moving average of gradient values + state["exp_avg"] = torch.zeros_like(p) + # Exponential moving average of squared gradient values + state["exp_avg_sq"] = torch.zeros_like(p) + # Exponential moving average of gradient difference + state["exp_avg_diff"] = torch.zeros_like(p) + + @torch.no_grad() + def step(self, closure=None): + """Performs a single optimization step.""" + + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + if self.defaults["max_grad_norm"] > 0: + device = self.param_groups[0]["params"][0].device + global_grad_norm = torch.zeros(1, device=device) + + max_grad_norm = torch.tensor(self.defaults["max_grad_norm"], device=device) + for group in self.param_groups: + for p in group["params"]: + if p.grad is not None: + grad = p.grad + global_grad_norm.add_(grad.pow(2).sum()) + + global_grad_norm = torch.sqrt(global_grad_norm) + + clip_global_grad_norm = torch.clamp( + max_grad_norm / (global_grad_norm + group["eps"]), max=1.0 + ).item() + else: + clip_global_grad_norm = 1.0 + + for group in self.param_groups: + params_with_grad = [] + grads = [] + exp_avgs = [] + exp_avg_sqs = [] + exp_avg_diffs = [] + neg_pre_grads = [] + + beta1, beta2, beta3 = group["betas"] + # assume same step across group now to simplify things + # per parameter step can be easily support + # by making it tensor, or pass list into kernel + if "step" in group: + group["step"] += 1 + else: + group["step"] = 1 + + bias_correction1 = 1.0 - beta1 ** group["step"] + bias_correction2 = 1.0 - beta2 ** group["step"] + bias_correction3 = 1.0 - beta3 ** group["step"] + + for p in group["params"]: + if p.grad is None: + continue + params_with_grad.append(p) + grads.append(p.grad) + + state = self.state[p] + if len(state) == 0: + state["exp_avg"] = torch.zeros_like(p) + state["exp_avg_sq"] = torch.zeros_like(p) + state["exp_avg_diff"] = torch.zeros_like(p) + + if "neg_pre_grad" not in state or group["step"] == 1: + state["neg_pre_grad"] = p.grad.clone().mul_(-clip_global_grad_norm) + + exp_avgs.append(state["exp_avg"]) + exp_avg_sqs.append(state["exp_avg_sq"]) + exp_avg_diffs.append(state["exp_avg_diff"]) + neg_pre_grads.append(state["neg_pre_grad"]) + + kwargs = dict( + params=params_with_grad, + grads=grads, + exp_avgs=exp_avgs, + exp_avg_sqs=exp_avg_sqs, + exp_avg_diffs=exp_avg_diffs, + neg_pre_grads=neg_pre_grads, + beta1=beta1, + beta2=beta2, + beta3=beta3, + bias_correction1=bias_correction1, + bias_correction2=bias_correction2, + bias_correction3_sqrt=math.sqrt(bias_correction3), + lr=group["lr"], + weight_decay=group["weight_decay"], + eps=group["eps"], + no_prox=group["no_prox"], + clip_global_grad_norm=clip_global_grad_norm, + ) + + if group["foreach"]: + _multi_tensor_adan(**kwargs) + else: + _single_tensor_adan(**kwargs) + + return loss + + +def _single_tensor_adan( + params: List[Tensor], + grads: List[Tensor], + exp_avgs: List[Tensor], + exp_avg_sqs: List[Tensor], + exp_avg_diffs: List[Tensor], + neg_pre_grads: List[Tensor], + *, + beta1: float, + beta2: float, + beta3: float, + bias_correction1: float, + bias_correction2: float, + bias_correction3_sqrt: float, + lr: float, + weight_decay: float, + eps: float, + no_prox: bool, + clip_global_grad_norm: Tensor, +): + for i, param in enumerate(params): + grad = grads[i] + exp_avg = exp_avgs[i] + exp_avg_sq = exp_avg_sqs[i] + exp_avg_diff = exp_avg_diffs[i] + neg_grad_or_diff = neg_pre_grads[i] + + grad.mul_(clip_global_grad_norm) + + # for memory saving, we use `neg_grad_or_diff` + # to get some temp variable in a inplace way + neg_grad_or_diff.add_(grad) + + exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) # m_t + exp_avg_diff.mul_(beta2).add_(neg_grad_or_diff, alpha=1 - beta2) # diff_t + + neg_grad_or_diff.mul_(beta2).add_(grad) + exp_avg_sq.mul_(beta3).addcmul_( + neg_grad_or_diff, neg_grad_or_diff, value=1 - beta3 + ) # n_t + + denom = ((exp_avg_sq).sqrt() / bias_correction3_sqrt).add_(eps) + step_size_diff = lr * beta2 / bias_correction2 + step_size = lr / bias_correction1 + + if no_prox: + param.mul_(1 - lr * weight_decay) + param.addcdiv_(exp_avg, denom, value=-step_size) + param.addcdiv_(exp_avg_diff, denom, value=-step_size_diff) + else: + param.addcdiv_(exp_avg, denom, value=-step_size) + param.addcdiv_(exp_avg_diff, denom, value=-step_size_diff) + param.div_(1 + lr * weight_decay) + + neg_grad_or_diff.zero_().add_(grad, alpha=-1.0) + + +def _multi_tensor_adan( + params: List[Tensor], + grads: List[Tensor], + exp_avgs: List[Tensor], + exp_avg_sqs: List[Tensor], + exp_avg_diffs: List[Tensor], + neg_pre_grads: List[Tensor], + *, + beta1: float, + beta2: float, + beta3: float, + bias_correction1: float, + bias_correction2: float, + bias_correction3_sqrt: float, + lr: float, + weight_decay: float, + eps: float, + no_prox: bool, + clip_global_grad_norm: Tensor, +): + if len(params) == 0: + return + + torch._foreach_mul_(grads, clip_global_grad_norm) + + # for memory saving, we use `neg_pre_grads` + # to get some temp variable in a inplace way + torch._foreach_add_(neg_pre_grads, grads) + + torch._foreach_mul_(exp_avgs, beta1) + torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1) # m_t + + torch._foreach_mul_(exp_avg_diffs, beta2) + torch._foreach_add_(exp_avg_diffs, neg_pre_grads, alpha=1 - beta2) # diff_t + + torch._foreach_mul_(neg_pre_grads, beta2) + torch._foreach_add_(neg_pre_grads, grads) + torch._foreach_mul_(exp_avg_sqs, beta3) + torch._foreach_addcmul_( + exp_avg_sqs, neg_pre_grads, neg_pre_grads, value=1 - beta3 + ) # n_t + + denom = torch._foreach_sqrt(exp_avg_sqs) + torch._foreach_div_(denom, bias_correction3_sqrt) + torch._foreach_add_(denom, eps) + + step_size_diff = lr * beta2 / bias_correction2 + step_size = lr / bias_correction1 + + if no_prox: + torch._foreach_mul_(params, 1 - lr * weight_decay) + torch._foreach_addcdiv_(params, exp_avgs, denom, value=-step_size) + torch._foreach_addcdiv_(params, exp_avg_diffs, denom, value=-step_size_diff) + else: + torch._foreach_addcdiv_(params, exp_avgs, denom, value=-step_size) + torch._foreach_addcdiv_(params, exp_avg_diffs, denom, value=-step_size_diff) + torch._foreach_div_(params, 1 + lr * weight_decay) + torch._foreach_zero_(neg_pre_grads) + torch._foreach_add_(neg_pre_grads, grads, alpha=-1.0) diff --git a/threestudio/systems/prolificdreamer.py b/threestudio/systems/prolificdreamer.py new file mode 100644 index 0000000..a29e9d2 --- /dev/null +++ b/threestudio/systems/prolificdreamer.py @@ -0,0 +1,234 @@ +import os +from dataclasses import dataclass, field + +import torch + +import threestudio +from threestudio.systems.base import BaseLift3DSystem +from threestudio.utils.misc import cleanup, get_device +from threestudio.utils.ops import binary_cross_entropy, dot +from threestudio.utils.typing import * + + +@threestudio.register("prolificdreamer-system") +class ProlificDreamer(BaseLift3DSystem): + @dataclass + class Config(BaseLift3DSystem.Config): + # in ['coarse', 'geometry', 'texture'] + stage: str = "coarse" + visualize_samples: bool = False + + cfg: Config + + def configure(self) -> None: + # set up geometry, material, background, renderer + super().configure() + self.guidance = threestudio.find(self.cfg.guidance_type)(self.cfg.guidance) + self.prompt_processor = threestudio.find(self.cfg.prompt_processor_type)( + self.cfg.prompt_processor + ) + self.prompt_utils = self.prompt_processor() + + def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]: + if self.cfg.stage == "geometry": + render_out = self.renderer(**batch, render_rgb=False) + else: + render_out = self.renderer(**batch) + return { + **render_out, + } + + def on_fit_start(self) -> None: + super().on_fit_start() + + def training_step(self, batch, batch_idx): + out = self(batch) + + if self.cfg.stage == "geometry": + guidance_inp = out["comp_normal"] + guidance_out = self.guidance( + guidance_inp, self.prompt_utils, **batch, rgb_as_latents=False + ) + else: + guidance_inp = out["comp_rgb"] + guidance_out = self.guidance( + guidance_inp, self.prompt_utils, **batch, rgb_as_latents=False + ) + + loss = 0.0 + + for name, value in guidance_out.items(): + if not (type(value) is torch.Tensor and value.numel() > 1): + self.log(f"train/{name}", value) + if name.startswith("loss_"): + loss += value * self.C(self.cfg.loss[name.replace("loss_", "lambda_")]) + + if self.cfg.stage == "coarse": + if self.C(self.cfg.loss.lambda_orient) > 0: + if "normal" not in out: + raise ValueError( + "Normal is required for orientation loss, no normal is found in the output." + ) + loss_orient = ( + out["weights"].detach() + * dot(out["normal"], out["t_dirs"]).clamp_min(0.0) ** 2 + ).sum() / (out["opacity"] > 0).sum() + self.log("train/loss_orient", loss_orient) + loss += loss_orient * self.C(self.cfg.loss.lambda_orient) + + loss_sparsity = (out["opacity"] ** 2 + 0.01).sqrt().mean() + self.log("train/loss_sparsity", loss_sparsity) + loss += loss_sparsity * self.C(self.cfg.loss.lambda_sparsity) + + opacity_clamped = out["opacity"].clamp(1.0e-3, 1.0 - 1.0e-3) + loss_opaque = binary_cross_entropy(opacity_clamped, opacity_clamped) + self.log("train/loss_opaque", loss_opaque) + loss += loss_opaque * self.C(self.cfg.loss.lambda_opaque) + + # z variance loss proposed in HiFA: http://arxiv.org/abs/2305.18766 + # helps reduce floaters and produce solid geometry + if "z_variance" in out: + loss_z_variance = out["z_variance"][out["opacity"] > 0.5].mean() + self.log("train/loss_z_variance", loss_z_variance) + loss += loss_z_variance * self.C(self.cfg.loss.lambda_z_variance) + + # sdf loss + if "sdf_grad" in out: + loss_eikonal = ( + (torch.linalg.norm(out["sdf_grad"], ord=2, dim=-1) - 1.0) ** 2 + ).mean() + self.log("train/loss_eikonal", loss_eikonal) + loss += loss_eikonal * self.C(self.cfg.loss.lambda_eikonal) + self.log("train/inv_std", out["inv_std"], prog_bar=True) + + elif self.cfg.stage == "geometry": + loss_normal_consistency = out["mesh"].normal_consistency() + self.log("train/loss_normal_consistency", loss_normal_consistency) + loss += loss_normal_consistency * self.C( + self.cfg.loss.lambda_normal_consistency + ) + + if self.C(self.cfg.loss.lambda_laplacian_smoothness) > 0: + loss_laplacian_smoothness = out["mesh"].laplacian() + self.log("train/loss_laplacian_smoothness", loss_laplacian_smoothness) + loss += loss_laplacian_smoothness * self.C( + self.cfg.loss.lambda_laplacian_smoothness + ) + elif self.cfg.stage == "texture": + pass + else: + raise ValueError(f"Unknown stage {self.cfg.stage}") + + for name, value in self.cfg.loss.items(): + self.log(f"train_params/{name}", self.C(value)) + + return {"loss": loss} + + def validation_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-{batch['index'][0]}.png", + ( + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + if "comp_rgb" in out + else [] + ) + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + name="validation_step", + step=self.true_global_step, + ) + + if self.cfg.visualize_samples: + self.save_image_grid( + f"it{self.true_global_step}-{batch['index'][0]}-sample.png", + [ + { + "type": "rgb", + "img": self.guidance.sample( + self.prompt_utils, **batch, seed=self.global_step + )[0], + "kwargs": {"data_format": "HWC"}, + }, + { + "type": "rgb", + "img": self.guidance.sample_lora(self.prompt_utils, **batch)[0], + "kwargs": {"data_format": "HWC"}, + }, + ], + name="validation_step_samples", + step=self.true_global_step, + ) + + def on_validation_epoch_end(self): + pass + + def test_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-test/{batch['index'][0]}.png", + ( + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + if "comp_rgb" in out + else [] + ) + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + name="test_step", + step=self.true_global_step, + ) + + def on_test_epoch_end(self): + self.save_img_sequence( + f"it{self.true_global_step}-test", + f"it{self.true_global_step}-test", + "(\d+)\.png", + save_format="mp4", + fps=30, + name="test", + step=self.true_global_step, + ) diff --git a/threestudio/systems/sjc.py b/threestudio/systems/sjc.py new file mode 100644 index 0000000..102c86a --- /dev/null +++ b/threestudio/systems/sjc.py @@ -0,0 +1,200 @@ +from dataclasses import dataclass, field + +import numpy as np +import torch + +import threestudio +from threestudio.systems.base import BaseLift3DSystem +from threestudio.utils.typing import * + + +@threestudio.register("sjc-system") +class ScoreJacobianChaining(BaseLift3DSystem): + @dataclass + class Config(BaseLift3DSystem.Config): + subpixel_rendering: bool = True + + cfg: Config + + def configure(self): + # create geometry, material, background, renderer + super().configure() + self.guidance = threestudio.find(self.cfg.guidance_type)(self.cfg.guidance) + + def forward(self, batch: Dict[str, Any], decode: bool = False) -> Dict[str, Any]: + render_out = self.renderer(**batch) + out = { + **render_out, + } + if decode: + if self.cfg.subpixel_rendering: + latent_height, latent_width = 128, 128 + else: + latent_height, latent_width = 64, 64 + out["decoded_rgb"] = self.guidance.decode_latents( + out["comp_rgb"].permute(0, 3, 1, 2), + latent_height=latent_height, + latent_width=latent_width, + ).permute(0, 2, 3, 1) + return out + + def on_fit_start(self) -> None: + super().on_fit_start() + # only used in training + self.prompt_processor = threestudio.find(self.cfg.prompt_processor_type)( + self.cfg.prompt_processor + ) + + def on_test_start(self) -> None: + # check if guidance is initialized, such as when loading from checkpoint + if not hasattr(self, "guidance"): + self.guidance = threestudio.find(self.cfg.guidance_type)(self.cfg.guidance) + + def training_step(self, batch, batch_idx): + out = self(batch) + prompt_utils = self.prompt_processor() + guidance_out = self.guidance( + out["comp_rgb"], prompt_utils, **batch, rgb_as_latents=True + ) + + loss = 0.0 + + for name, value in guidance_out.items(): + self.log(f"train/{name}", value) + if name.startswith("loss_"): + loss += value * self.C(self.cfg.loss[name.replace("loss_", "lambda_")]) + + loss_emptiness = ( + self.C(self.cfg.loss.lambda_emptiness) + * torch.log(1 + self.cfg.loss.emptiness_scale * out["weights"]).mean() + ) + + self.log("train/loss_emptiness", loss_emptiness) + loss += loss_emptiness + + # About the depth loss, see https://github.com/pals-ttic/sjc/issues/21 + if self.C(self.cfg.loss.lambda_depth) > 0: + _, h, w, _ = out["comp_rgb"].shape + comp_depth = (out["depth"] + 10 * (1 - out["opacity"])).squeeze(-1) + center_h = int(self.cfg.loss.center_ratio * h) + center_w = int(self.cfg.loss.center_ratio * w) + border_h = (h - center_h) // 2 + border_w = (h - center_w) // 2 + center_depth = comp_depth[ + ..., border_h : border_h + center_h, border_w : border_w + center_w + ] + center_depth_mean = center_depth.mean() + border_depth_mean = (comp_depth.sum() - center_depth.sum()) / ( + h * w - center_h * center_w + ) + log_input = center_depth_mean - border_depth_mean + 1e-12 + loss_depth = ( + torch.sign(log_input) + * torch.log(log_input) + * self.C(self.cfg.loss.lambda_depth) + ) + + self.log("train/loss_depth", loss_depth) + loss += loss_depth + + for name, value in self.cfg.loss.items(): + self.log(f"train_params/{name}", self.C(value)) + + return {"loss": loss} + + def vis_depth(self, pred_depth): + depth = pred_depth.detach().cpu().numpy() + depth = np.log(1.0 + depth + 1e-12) / np.log(1 + 10.0) + return depth + + def validation_step(self, batch, batch_idx): + out = self(batch, decode=True) + comp_depth = out["depth"] + 10 * (1 - out["opacity"]) # 10 for background + vis_depth = self.vis_depth(comp_depth.squeeze(-1)) + + self.save_image_grid( + f"it{self.true_global_step}-{batch['index'][0]}.png", + [ + { + "type": "rgb", + "img": out["decoded_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ] + + [ + { + "type": "grayscale", + "img": vis_depth[0], + "kwargs": {"cmap": "spectral", "data_range": (0, 1)}, + }, + ], + align=512, + name="validation_step", + step=self.true_global_step, + ) + + def on_validation_epoch_end(self): + pass + + def test_step(self, batch, batch_idx): + out = self(batch, decode=True) + self.save_image_grid( + f"it{self.true_global_step}-test/{batch['index'][0]}.png", + [ + { + "type": "rgb", + "img": out["decoded_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + align=512, + name="test_step", + step=self.true_global_step, + ) + + def on_test_epoch_end(self): + self.save_img_sequence( + f"it{self.true_global_step}-test", + f"it{self.true_global_step}-test", + "(\d+)\.png", + save_format="mp4", + fps=30, + name="test", + step=self.true_global_step, + ) diff --git a/threestudio/systems/textmesh.py b/threestudio/systems/textmesh.py new file mode 100644 index 0000000..d5f0d65 --- /dev/null +++ b/threestudio/systems/textmesh.py @@ -0,0 +1,160 @@ +from dataclasses import dataclass, field + +import torch + +import threestudio +from threestudio.systems.base import BaseLift3DSystem +from threestudio.utils.ops import binary_cross_entropy, dot +from threestudio.utils.typing import * + + +@threestudio.register("textmesh-system") +class TextMesh(BaseLift3DSystem): + @dataclass + class Config(BaseLift3DSystem.Config): + pass + + cfg: Config + + def configure(self): + # create geometry, material, background, renderer + super().configure() + + def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]: + render_out = self.renderer(**batch) + return { + **render_out, + } + + def on_fit_start(self) -> None: + super().on_fit_start() + # only used in training + self.prompt_processor = threestudio.find(self.cfg.prompt_processor_type)( + self.cfg.prompt_processor + ) + self.guidance = threestudio.find(self.cfg.guidance_type)(self.cfg.guidance) + + # initialize SDF + self.geometry.initialize_shape() + + def training_step(self, batch, batch_idx): + out = self(batch) + prompt_utils = self.prompt_processor() + guidance_out = self.guidance( + out["comp_rgb"], prompt_utils, **batch, rgb_as_latents=False + ) + + loss = 0.0 + + for name, value in guidance_out.items(): + self.log(f"train/{name}", value) + if name.startswith("loss_"): + loss += value * self.C(self.cfg.loss[name.replace("loss_", "lambda_")]) + + if self.C(self.cfg.loss.lambda_orient) > 0: + if "normal" not in out: + raise ValueError( + "Normal is required for orientation loss, no normal is found in the output." + ) + loss_orient = ( + out["weights"].detach() + * dot(out["normal"], out["t_dirs"]).clamp_min(0.0) ** 2 + ).sum() / (out["opacity"] > 0).sum() + self.log("train/loss_orient", loss_orient) + loss += loss_orient * self.C(self.cfg.loss.lambda_orient) + + loss_sparsity = (out["opacity"] ** 2 + 0.01).sqrt().mean() + self.log("train/loss_sparsity", loss_sparsity) + loss += loss_sparsity * self.C(self.cfg.loss.lambda_sparsity) + + opacity_clamped = out["opacity"].clamp(1.0e-3, 1.0 - 1.0e-3) + loss_opaque = binary_cross_entropy(opacity_clamped, opacity_clamped) + self.log("train/loss_opaque", loss_opaque) + loss += loss_opaque * self.C(self.cfg.loss.lambda_opaque) + + loss_eikonal = ( + (torch.linalg.norm(out["sdf_grad"], ord=2, dim=-1) - 1.0) ** 2 + ).mean() + self.log("train/loss_eikonal", loss_eikonal) + loss += loss_eikonal * self.C(self.cfg.loss.lambda_eikonal) + + self.log("train/inv_std", out["inv_std"], prog_bar=True) + + for name, value in self.cfg.loss.items(): + self.log(f"train_params/{name}", self.C(value)) + + return {"loss": loss} + + def validation_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-{batch['index'][0]}.png", + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + ) + + def on_validation_epoch_end(self): + pass + + def test_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-test/{batch['index'][0]}.png", + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + ) + + def on_test_epoch_end(self): + self.save_img_sequence( + f"it{self.true_global_step}-test", + f"it{self.true_global_step}-test", + "(\d+)\.png", + save_format="mp4", + fps=30, + ) diff --git a/threestudio/systems/utils.py b/threestudio/systems/utils.py new file mode 100644 index 0000000..732b369 --- /dev/null +++ b/threestudio/systems/utils.py @@ -0,0 +1,104 @@ +import sys +import warnings +from bisect import bisect_right + +import torch +import torch.nn as nn +from torch.optim import lr_scheduler + +import threestudio + + +def get_scheduler(name): + if hasattr(lr_scheduler, name): + return getattr(lr_scheduler, name) + else: + raise NotImplementedError + + +def getattr_recursive(m, attr): + for name in attr.split("."): + m = getattr(m, name) + return m + + +def get_parameters(model, name): + module = getattr_recursive(model, name) + if isinstance(module, nn.Module): + return module.parameters() + elif isinstance(module, nn.Parameter): + return module + return [] + + +def parse_optimizer(config, model): + if hasattr(config, "params"): + params = [ + {"params": get_parameters(model, name), "name": name, **args} + for name, args in config.params.items() + ] + threestudio.debug(f"Specify optimizer params: {config.params}") + else: + params = model.parameters() + if config.name in ["FusedAdam"]: + import apex + + optim = getattr(apex.optimizers, config.name)(params, **config.args) + elif config.name in ["Adan"]: + from threestudio.systems import optimizers + + optim = getattr(optimizers, config.name)(params, **config.args) + else: + optim = getattr(torch.optim, config.name)(params, **config.args) + return optim + + +def parse_scheduler_to_instance(config, optimizer): + if config.name == "ChainedScheduler": + schedulers = [ + parse_scheduler_to_instance(conf, optimizer) for conf in config.schedulers + ] + scheduler = lr_scheduler.ChainedScheduler(schedulers) + elif config.name == "Sequential": + schedulers = [ + parse_scheduler_to_instance(conf, optimizer) for conf in config.schedulers + ] + scheduler = lr_scheduler.SequentialLR( + optimizer, schedulers, milestones=config.milestones + ) + else: + scheduler = getattr(lr_scheduler, config.name)(optimizer, **config.args) + return scheduler + + +def parse_scheduler(config, optimizer): + interval = config.get("interval", "epoch") + assert interval in ["epoch", "step"] + if config.name == "SequentialLR": + scheduler = { + "scheduler": lr_scheduler.SequentialLR( + optimizer, + [ + parse_scheduler(conf, optimizer)["scheduler"] + for conf in config.schedulers + ], + milestones=config.milestones, + ), + "interval": interval, + } + elif config.name == "ChainedScheduler": + scheduler = { + "scheduler": lr_scheduler.ChainedScheduler( + [ + parse_scheduler(conf, optimizer)["scheduler"] + for conf in config.schedulers + ] + ), + "interval": interval, + } + else: + scheduler = { + "scheduler": get_scheduler(config.name)(optimizer, **config.args), + "interval": interval, + } + return scheduler diff --git a/threestudio/systems/zero123.py b/threestudio/systems/zero123.py new file mode 100644 index 0000000..320b3e6 --- /dev/null +++ b/threestudio/systems/zero123.py @@ -0,0 +1,390 @@ +import os +import random +import shutil +from dataclasses import dataclass, field + +import torch +import torch.nn.functional as F +from PIL import Image, ImageDraw +from torchmetrics import PearsonCorrCoef + +import threestudio +from threestudio.systems.base import BaseLift3DSystem +from threestudio.utils.ops import binary_cross_entropy, dot +from threestudio.utils.typing import * + + +@threestudio.register("zero123-system") +class Zero123(BaseLift3DSystem): + @dataclass + class Config(BaseLift3DSystem.Config): + freq: dict = field(default_factory=dict) + refinement: bool = False + ambient_ratio_min: float = 0.5 + + cfg: Config + + def configure(self): + # create geometry, material, background, renderer + super().configure() + + def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]: + render_out = self.renderer(**batch) + return { + **render_out, + } + + def on_fit_start(self) -> None: + super().on_fit_start() + # no prompt processor + self.guidance = threestudio.find(self.cfg.guidance_type)(self.cfg.guidance) + + # visualize all training images + all_images = self.trainer.datamodule.train_dataloader().dataset.get_all_images() + self.save_image_grid( + "all_training_images.png", + [ + {"type": "rgb", "img": image, "kwargs": {"data_format": "HWC"}} + for image in all_images + ], + name="on_fit_start", + step=self.true_global_step, + ) + + self.pearson = PearsonCorrCoef().to(self.device) + + def training_substep(self, batch, batch_idx, guidance: str): + """ + Args: + guidance: one of "ref" (reference image supervision), "zero123" + """ + if guidance == "ref": + # bg_color = torch.rand_like(batch['rays_o']) + ambient_ratio = 1.0 + shading = "diffuse" + batch["shading"] = shading + elif guidance == "zero123": + batch = batch["random_camera"] + ambient_ratio = ( + self.cfg.ambient_ratio_min + + (1 - self.cfg.ambient_ratio_min) * random.random() + ) + + batch["bg_color"] = None + batch["ambient_ratio"] = ambient_ratio + + out = self(batch) + loss_prefix = f"loss_{guidance}_" + + loss_terms = {} + + def set_loss(name, value): + loss_terms[f"{loss_prefix}{name}"] = value + + guidance_eval = ( + guidance == "zero123" + and self.cfg.freq.guidance_eval > 0 + and self.true_global_step % self.cfg.freq.guidance_eval == 0 + ) + + if guidance == "ref": + gt_mask = batch["mask"] + gt_rgb = batch["rgb"] + + # color loss + gt_rgb = gt_rgb * gt_mask.float() + out["comp_rgb_bg"] * ( + 1 - gt_mask.float() + ) + set_loss("rgb", F.mse_loss(gt_rgb, out["comp_rgb"])) + + # mask loss + set_loss("mask", F.mse_loss(gt_mask.float(), out["opacity"])) + + # depth loss + if self.C(self.cfg.loss.lambda_depth) > 0: + valid_gt_depth = batch["ref_depth"][gt_mask.squeeze(-1)].unsqueeze(1) + valid_pred_depth = out["depth"][gt_mask].unsqueeze(1) + with torch.no_grad(): + A = torch.cat( + [valid_gt_depth, torch.ones_like(valid_gt_depth)], dim=-1 + ) # [B, 2] + X = torch.linalg.lstsq(A, valid_pred_depth).solution # [2, 1] + valid_gt_depth = A @ X # [B, 1] + set_loss("depth", F.mse_loss(valid_gt_depth, valid_pred_depth)) + + # relative depth loss + if self.C(self.cfg.loss.lambda_depth_rel) > 0: + valid_gt_depth = batch["ref_depth"][gt_mask.squeeze(-1)] # [B,] + valid_pred_depth = out["depth"][gt_mask] # [B,] + set_loss( + "depth_rel", 1 - self.pearson(valid_pred_depth, valid_gt_depth) + ) + + # normal loss + if self.C(self.cfg.loss.lambda_normal) > 0: + valid_gt_normal = ( + 1 - 2 * batch["ref_normal"][gt_mask.squeeze(-1)] + ) # [B, 3] + valid_pred_normal = ( + 2 * out["comp_normal"][gt_mask.squeeze(-1)] - 1 + ) # [B, 3] + set_loss( + "normal", + 1 - F.cosine_similarity(valid_pred_normal, valid_gt_normal).mean(), + ) + elif guidance == "zero123": + # zero123 + guidance_out = self.guidance( + out["comp_rgb"], + **batch, + rgb_as_latents=False, + guidance_eval=guidance_eval, + ) + # claforte: TODO: rename the loss_terms keys + set_loss("sds", guidance_out["loss_sds"]) + + if self.C(self.cfg.loss.lambda_normal_smooth) > 0: + if "comp_normal" not in out: + raise ValueError( + "comp_normal is required for 2D normal smooth loss, no comp_normal is found in the output." + ) + normal = out["comp_normal"] + set_loss( + "normal_smooth", + (normal[:, 1:, :, :] - normal[:, :-1, :, :]).square().mean() + + (normal[:, :, 1:, :] - normal[:, :, :-1, :]).square().mean(), + ) + + if self.C(self.cfg.loss.lambda_3d_normal_smooth) > 0: + if "normal" not in out: + raise ValueError( + "Normal is required for normal smooth loss, no normal is found in the output." + ) + if "normal_perturb" not in out: + raise ValueError( + "normal_perturb is required for normal smooth loss, no normal_perturb is found in the output." + ) + normals = out["normal"] + normals_perturb = out["normal_perturb"] + set_loss("3d_normal_smooth", (normals - normals_perturb).abs().mean()) + + if not self.cfg.refinement: + if self.C(self.cfg.loss.lambda_orient) > 0: + if "normal" not in out: + raise ValueError( + "Normal is required for orientation loss, no normal is found in the output." + ) + set_loss( + "orient", + ( + out["weights"].detach() + * dot(out["normal"], out["t_dirs"]).clamp_min(0.0) ** 2 + ).sum() + / (out["opacity"] > 0).sum(), + ) + + if guidance != "ref" and self.C(self.cfg.loss.lambda_sparsity) > 0: + set_loss("sparsity", (out["opacity"] ** 2 + 0.01).sqrt().mean()) + + if self.C(self.cfg.loss.lambda_opaque) > 0: + opacity_clamped = out["opacity"].clamp(1.0e-3, 1.0 - 1.0e-3) + set_loss( + "opaque", binary_cross_entropy(opacity_clamped, opacity_clamped) + ) + else: + if self.C(self.cfg.loss.lambda_normal_consistency) > 0: + set_loss("normal_consistency", out["mesh"].normal_consistency()) + if self.C(self.cfg.loss.lambda_laplacian_smoothness) > 0: + set_loss("laplacian_smoothness", out["mesh"].laplacian()) + + loss = 0.0 + for name, value in loss_terms.items(): + self.log(f"train/{name}", value) + if name.startswith(loss_prefix): + loss_weighted = value * self.C( + self.cfg.loss[name.replace(loss_prefix, "lambda_")] + ) + self.log(f"train/{name}_w", loss_weighted) + loss += loss_weighted + + for name, value in self.cfg.loss.items(): + self.log(f"train_params/{name}", self.C(value)) + + self.log(f"train/loss_{guidance}", loss) + + if guidance_eval: + self.guidance_evaluation_save( + out["comp_rgb"].detach()[: guidance_out["eval"]["bs"]], + guidance_out["eval"], + ) + + return {"loss": loss} + + def training_step(self, batch, batch_idx): + if self.cfg.freq.get("ref_or_zero123", "accumulate") == "accumulate": + do_ref = True + do_zero123 = True + elif self.cfg.freq.get("ref_or_zero123", "accumulate") == "alternate": + do_ref = ( + self.true_global_step < self.cfg.freq.ref_only_steps + or self.true_global_step % self.cfg.freq.n_ref == 0 + ) + do_zero123 = not do_ref + + total_loss = 0.0 + if do_zero123: + out = self.training_substep(batch, batch_idx, guidance="zero123") + total_loss += out["loss"] + + if do_ref: + out = self.training_substep(batch, batch_idx, guidance="ref") + total_loss += out["loss"] + + self.log("train/loss", total_loss, prog_bar=True) + + # sch = self.lr_schedulers() + # sch.step() + + return {"loss": total_loss} + + def validation_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-val/{batch['index'][0]}.png", + ( + [ + { + "type": "rgb", + "img": batch["rgb"][0], + "kwargs": {"data_format": "HWC"}, + } + ] + if "rgb" in batch + else [] + ) + + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + ( + [ + { + "type": "grayscale", + "img": out["depth"][0], + "kwargs": {}, + } + ] + if "depth" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + # claforte: TODO: don't hardcode the frame numbers to record... read them from cfg instead. + name=f"validation_step_batchidx_{batch_idx}" + if batch_idx in [0, 7, 15, 23, 29] + else None, + step=self.true_global_step, + ) + + def on_validation_epoch_end(self): + filestem = f"it{self.true_global_step}-val" + self.save_img_sequence( + filestem, + filestem, + "(\d+)\.png", + save_format="mp4", + fps=30, + name="validation_epoch_end", + step=self.true_global_step, + ) + shutil.rmtree( + os.path.join(self.get_save_dir(), f"it{self.true_global_step}-val") + ) + + def test_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-test/{batch['index'][0]}.png", + ( + [ + { + "type": "rgb", + "img": batch["rgb"][0], + "kwargs": {"data_format": "HWC"}, + } + ] + if "rgb" in batch + else [] + ) + + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + ( + [ + { + "type": "grayscale", + "img": out["depth"][0], + "kwargs": {}, + } + ] + if "depth" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + name="test_step", + step=self.true_global_step, + ) + + def on_test_epoch_end(self): + self.save_img_sequence( + f"it{self.true_global_step}-test", + f"it{self.true_global_step}-test", + "(\d+)\.png", + save_format="mp4", + fps=30, + name="test", + step=self.true_global_step, + ) + shutil.rmtree( + os.path.join(self.get_save_dir(), f"it{self.true_global_step}-test") + ) diff --git a/threestudio/systems/zero123_simple.py b/threestudio/systems/zero123_simple.py new file mode 100644 index 0000000..f1f5d05 --- /dev/null +++ b/threestudio/systems/zero123_simple.py @@ -0,0 +1,207 @@ +from dataclasses import dataclass, field + +import torch + +import threestudio +from threestudio.systems.base import BaseLift3DSystem +from threestudio.utils.ops import binary_cross_entropy, dot +from threestudio.utils.typing import * + + +@threestudio.register("zero123-simple-system") +class Zero123Simple(BaseLift3DSystem): + @dataclass + class Config(BaseLift3DSystem.Config): + pass + + cfg: Config + + def configure(self): + # create geometry, material, background, renderer + super().configure() + self.guidance = threestudio.find(self.cfg.guidance_type)(self.cfg.guidance) + + def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]: + render_out = self.renderer(**batch) + return { + **render_out, + } + + def on_fit_start(self) -> None: + super().on_fit_start() + + def training_step(self, batch, batch_idx): + out = self(batch["random_camera"]) + guidance_out = self.guidance( + out["comp_rgb"], + **batch["random_camera"], + rgb_as_latents=False, + ) + + loss = 0.0 + + for name, value in guidance_out.items(): + if not (isinstance(value, torch.Tensor) and len(value.shape) > 0): + self.log(f"train/{name}", value) + if name.startswith("loss_"): + loss += value * self.C(self.cfg.loss[name.replace("loss_", "lambda_")]) + + if self.C(self.cfg.loss.lambda_orient) > 0: + if "normal" not in out: + raise ValueError( + "Normal is required for orientation loss, no normal is found in the output." + ) + loss_orient = ( + out["weights"].detach() + * dot(out["normal"], out["t_dirs"]).clamp_min(0.0) ** 2 + ).sum() / (out["opacity"] > 0).sum() + self.log("train/loss_orient", loss_orient) + loss += loss_orient * self.C(self.cfg.loss.lambda_orient) + + if self.C(self.cfg.loss.lambda_normal_smoothness_2d) > 0: + if "comp_normal" not in out: + raise ValueError( + "comp_normal is required for 2D normal smoothness loss, no comp_normal is found in the output." + ) + normal = out["comp_normal"] + loss_normal_smoothness_2d = ( + normal[:, 1:, :, :] - normal[:, :-1, :, :] + ).square().mean() + ( + normal[:, :, 1:, :] - normal[:, :, :-1, :] + ).square().mean() + self.log("trian/loss_normal_smoothness_2d", loss_normal_smoothness_2d) + loss += loss_normal_smoothness_2d * self.C( + self.cfg.loss.lambda_normal_smoothness_2d + ) + + loss_sparsity = (out["opacity"] ** 2 + 0.01).sqrt().mean() + self.log("train/loss_sparsity", loss_sparsity) + loss += loss_sparsity * self.C(self.cfg.loss.lambda_sparsity) + + opacity_clamped = out["opacity"].clamp(1.0e-3, 1.0 - 1.0e-3) + loss_opaque = binary_cross_entropy(opacity_clamped, opacity_clamped) + self.log("train/loss_opaque", loss_opaque) + loss += loss_opaque * self.C(self.cfg.loss.lambda_opaque) + + for name, value in self.cfg.loss.items(): + self.log(f"train_params/{name}", self.C(value)) + + if self.true_global_step % 50 == 0: + self.save_image_grid( + f"it{self.true_global_step}-train-t{int(guidance_out['timesteps'][0])}.png", + ( + [ + { + "type": "rgb", + "img": guidance_out["rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + if "rgb" in guidance_out + else [] + ) + + ( + [ + { + "type": "rgb", + "img": guidance_out["rgb_1step_orig"][0], + "kwargs": {"data_format": "HWC"}, + } + ] + if "rgb_1step_orig" in guidance_out + else [] + ) + + ( + [ + { + "type": "rgb", + "img": guidance_out["rgb_multistep_orig"][0], + "kwargs": {"data_format": "HWC"}, + } + ] + if "rgb_multistep_orig" in guidance_out + else [] + ), + ) + + return {"loss": loss} + + def validation_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-{batch['index'][0]}.png", + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + name="validation_step", + step=self.true_global_step, + ) + + def on_validation_epoch_end(self): + pass + + def test_step(self, batch, batch_idx): + out = self(batch) + self.save_image_grid( + f"it{self.true_global_step}-test/{batch['index'][0]}.png", + [ + { + "type": "rgb", + "img": out["comp_rgb"][0], + "kwargs": {"data_format": "HWC"}, + }, + ] + + ( + [ + { + "type": "rgb", + "img": out["comp_normal"][0], + "kwargs": {"data_format": "HWC", "data_range": (0, 1)}, + } + ] + if "comp_normal" in out + else [] + ) + + [ + { + "type": "grayscale", + "img": out["opacity"][0, :, :, 0], + "kwargs": {"cmap": None, "data_range": (0, 1)}, + }, + ], + name="test_step", + step=self.true_global_step, + ) + + def on_test_epoch_end(self): + self.save_img_sequence( + f"it{self.true_global_step}-test", + f"it{self.true_global_step}-test", + "(\d+)\.png", + save_format="mp4", + fps=30, + name="test", + step=self.true_global_step, + ) diff --git a/threestudio/utils/GAN/__init__.py b/threestudio/utils/GAN/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/threestudio/utils/GAN/attention.py b/threestudio/utils/GAN/attention.py new file mode 100644 index 0000000..c9a1a95 --- /dev/null +++ b/threestudio/utils/GAN/attention.py @@ -0,0 +1,278 @@ +import math +from inspect import isfunction + +import torch +import torch.nn.functional as F +from einops import rearrange, repeat +from torch import einsum, nn + +from threestudio.utils.GAN.network_util import checkpoint + + +def exists(val): + return val is not None + + +def uniq(arr): + return {el: True for el in arr}.keys() + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def max_neg_value(t): + return -torch.finfo(t.dtype).max + + +def init_(tensor): + dim = tensor.shape[-1] + std = 1 / math.sqrt(dim) + tensor.uniform_(-std, std) + return tensor + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = ( + nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU()) + if not glu + else GEGLU(dim, inner_dim) + ) + + self.net = nn.Sequential( + project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def Normalize(in_channels): + return torch.nn.GroupNorm( + num_groups=32, num_channels=in_channels, eps=1e-6, affine=True + ) + + +class LinearAttention(nn.Module): + def __init__(self, dim, heads=4, dim_head=32): + super().__init__() + self.heads = heads + hidden_dim = dim_head * heads + self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False) + self.to_out = nn.Conv2d(hidden_dim, dim, 1) + + def forward(self, x): + b, c, h, w = x.shape + qkv = self.to_qkv(x) + q, k, v = rearrange( + qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3 + ) + k = k.softmax(dim=-1) + context = torch.einsum("bhdn,bhen->bhde", k, v) + out = torch.einsum("bhde,bhdn->bhen", context, q) + out = rearrange( + out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w + ) + return self.to_out(out) + + +class SpatialSelfAttention(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = rearrange(q, "b c h w -> b (h w) c") + k = rearrange(k, "b c h w -> b c (h w)") + w_ = torch.einsum("bij,bjk->bik", q, k) + + w_ = w_ * (int(c) ** (-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = rearrange(v, "b c h w -> b c (h w)") + w_ = rearrange(w_, "b i j -> b j i") + h_ = torch.einsum("bij,bjk->bik", v, w_) + h_ = rearrange(h_, "b c (h w) -> b c h w", h=h) + h_ = self.proj_out(h_) + + return x + h_ + + +class CrossAttention(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.scale = dim_head**-0.5 + self.heads = heads + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, query_dim), nn.Dropout(dropout) + ) + + def forward(self, x, context=None, mask=None): + h = self.heads + + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + v = self.to_v(context) + + q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v)) + + sim = einsum("b i d, b j d -> b i j", q, k) * self.scale + + if exists(mask): + mask = rearrange(mask, "b ... -> b (...)") + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, "b j -> (b h) () j", h=h) + sim.masked_fill_(~mask, max_neg_value) + + # attention, what we cannot get enough of + attn = sim.softmax(dim=-1) + + out = einsum("b i j, b j d -> b i d", attn, v) + out = rearrange(out, "(b h) n d -> b n (h d)", h=h) + return self.to_out(out) + + +class BasicTransformerBlock(nn.Module): + def __init__( + self, + dim, + n_heads, + d_head, + dropout=0.0, + context_dim=None, + gated_ff=True, + checkpoint=True, + ): + super().__init__() + self.attn1 = CrossAttention( + query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout + ) # is a self-attention + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = CrossAttention( + query_dim=dim, + context_dim=context_dim, + heads=n_heads, + dim_head=d_head, + dropout=dropout, + ) # is self-attn if context is none + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + self.checkpoint = checkpoint + + def forward(self, x, context=None): + return checkpoint( + self._forward, (x, context), self.parameters(), self.checkpoint + ) + + def _forward(self, x, context=None): + x = self.attn1(self.norm1(x)) + x + x = self.attn2(self.norm2(x), context=context) + x + x = self.ff(self.norm3(x)) + x + return x + + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + """ + + def __init__( + self, in_channels, n_heads, d_head, depth=1, dropout=0.0, context_dim=None + ): + super().__init__() + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = Normalize(in_channels) + + self.proj_in = nn.Conv2d( + in_channels, inner_dim, kernel_size=1, stride=1, padding=0 + ) + + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim + ) + for d in range(depth) + ] + ) + + self.proj_out = zero_module( + nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) + ) + + def forward(self, x, context=None): + # note: if no context is given, cross-attention defaults to self-attention + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + x = self.proj_in(x) + x = rearrange(x, "b c h w -> b (h w) c") + for block in self.transformer_blocks: + x = block(x, context=context) + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) + x = self.proj_out(x) + return x + x_in diff --git a/threestudio/utils/GAN/discriminator.py b/threestudio/utils/GAN/discriminator.py new file mode 100644 index 0000000..d3fdfe7 --- /dev/null +++ b/threestudio/utils/GAN/discriminator.py @@ -0,0 +1,217 @@ +import functools + +import torch +import torch.nn as nn + + +def count_params(model): + total_params = sum(p.numel() for p in model.parameters()) + return total_params + + +class ActNorm(nn.Module): + def __init__( + self, num_features, logdet=False, affine=True, allow_reverse_init=False + ): + assert affine + super().__init__() + self.logdet = logdet + self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1)) + self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1)) + self.allow_reverse_init = allow_reverse_init + + self.register_buffer("initialized", torch.tensor(0, dtype=torch.uint8)) + + def initialize(self, input): + with torch.no_grad(): + flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1) + mean = ( + flatten.mean(1) + .unsqueeze(1) + .unsqueeze(2) + .unsqueeze(3) + .permute(1, 0, 2, 3) + ) + std = ( + flatten.std(1) + .unsqueeze(1) + .unsqueeze(2) + .unsqueeze(3) + .permute(1, 0, 2, 3) + ) + + self.loc.data.copy_(-mean) + self.scale.data.copy_(1 / (std + 1e-6)) + + def forward(self, input, reverse=False): + if reverse: + return self.reverse(input) + if len(input.shape) == 2: + input = input[:, :, None, None] + squeeze = True + else: + squeeze = False + + _, _, height, width = input.shape + + if self.training and self.initialized.item() == 0: + self.initialize(input) + self.initialized.fill_(1) + + h = self.scale * (input + self.loc) + + if squeeze: + h = h.squeeze(-1).squeeze(-1) + + if self.logdet: + log_abs = torch.log(torch.abs(self.scale)) + logdet = height * width * torch.sum(log_abs) + logdet = logdet * torch.ones(input.shape[0]).to(input) + return h, logdet + + return h + + def reverse(self, output): + if self.training and self.initialized.item() == 0: + if not self.allow_reverse_init: + raise RuntimeError( + "Initializing ActNorm in reverse direction is " + "disabled by default. Use allow_reverse_init=True to enable." + ) + else: + self.initialize(output) + self.initialized.fill_(1) + + if len(output.shape) == 2: + output = output[:, :, None, None] + squeeze = True + else: + squeeze = False + + h = output / self.scale - self.loc + + if squeeze: + h = h.squeeze(-1).squeeze(-1) + return h + + +class AbstractEncoder(nn.Module): + def __init__(self): + super().__init__() + + def encode(self, *args, **kwargs): + raise NotImplementedError + + +class Labelator(AbstractEncoder): + """Net2Net Interface for Class-Conditional Model""" + + def __init__(self, n_classes, quantize_interface=True): + super().__init__() + self.n_classes = n_classes + self.quantize_interface = quantize_interface + + def encode(self, c): + c = c[:, None] + if self.quantize_interface: + return c, None, [None, None, c.long()] + return c + + +class SOSProvider(AbstractEncoder): + # for unconditional training + def __init__(self, sos_token, quantize_interface=True): + super().__init__() + self.sos_token = sos_token + self.quantize_interface = quantize_interface + + def encode(self, x): + # get batch size from data and replicate sos_token + c = torch.ones(x.shape[0], 1) * self.sos_token + c = c.long().to(x.device) + if self.quantize_interface: + return c, None, [None, None, c] + return c + + +def weights_init(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + nn.init.normal_(m.weight.data, 0.0, 0.02) + elif classname.find("BatchNorm") != -1: + nn.init.normal_(m.weight.data, 1.0, 0.02) + nn.init.constant_(m.bias.data, 0) + + +class NLayerDiscriminator(nn.Module): + """Defines a PatchGAN discriminator as in Pix2Pix + --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py + """ + + def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False): + """Construct a PatchGAN discriminator + Parameters: + input_nc (int) -- the number of channels in input images + ndf (int) -- the number of filters in the last conv layer + n_layers (int) -- the number of conv layers in the discriminator + norm_layer -- normalization layer + """ + super(NLayerDiscriminator, self).__init__() + if not use_actnorm: + norm_layer = nn.BatchNorm2d + else: + norm_layer = ActNorm + if ( + type(norm_layer) == functools.partial + ): # no need to use bias as BatchNorm2d has affine parameters + use_bias = norm_layer.func != nn.BatchNorm2d + else: + use_bias = norm_layer != nn.BatchNorm2d + + kw = 4 + padw = 1 + sequence = [ + nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), + nn.LeakyReLU(0.2, True), + ] + nf_mult = 1 + nf_mult_prev = 1 + for n in range(1, n_layers): # gradually increase the number of filters + nf_mult_prev = nf_mult + nf_mult = min(2**n, 8) + sequence += [ + nn.Conv2d( + ndf * nf_mult_prev, + ndf * nf_mult, + kernel_size=kw, + stride=2, + padding=padw, + bias=use_bias, + ), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True), + ] + + nf_mult_prev = nf_mult + nf_mult = min(2**n_layers, 8) + sequence += [ + nn.Conv2d( + ndf * nf_mult_prev, + ndf * nf_mult, + kernel_size=kw, + stride=1, + padding=padw, + bias=use_bias, + ), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True), + ] + + sequence += [ + nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw) + ] # output 1 channel prediction map + self.main = nn.Sequential(*sequence) + + def forward(self, input): + """Standard forward.""" + return self.main(input) diff --git a/threestudio/utils/GAN/distribution.py b/threestudio/utils/GAN/distribution.py new file mode 100644 index 0000000..016be35 --- /dev/null +++ b/threestudio/utils/GAN/distribution.py @@ -0,0 +1,102 @@ +import numpy as np +import torch + + +class AbstractDistribution: + def sample(self): + raise NotImplementedError() + + def mode(self): + raise NotImplementedError() + + +class DiracDistribution(AbstractDistribution): + def __init__(self, value): + self.value = value + + def sample(self): + return self.value + + def mode(self): + return self.value + + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean).to( + device=self.parameters.device + ) + + def sample(self): + x = self.mean + self.std * torch.randn(self.mean.shape).to( + device=self.parameters.device + ) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.0]) + else: + if other is None: + return 0.5 * torch.sum( + torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, + dim=[1, 2, 3], + ) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var + - 1.0 + - self.logvar + + other.logvar, + dim=[1, 2, 3], + ) + + def nll(self, sample, dims=[1, 2, 3]): + if self.deterministic: + return torch.Tensor([0.0]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims, + ) + + def mode(self): + return self.mean + + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 + Compute the KL divergence between two gaussians. + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, torch.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for torch.exp(). + logvar1, logvar2 = [ + x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) + for x in (logvar1, logvar2) + ] + + return 0.5 * ( + -1.0 + + logvar2 + - logvar1 + + torch.exp(logvar1 - logvar2) + + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) + ) diff --git a/threestudio/utils/GAN/loss.py b/threestudio/utils/GAN/loss.py new file mode 100644 index 0000000..7758065 --- /dev/null +++ b/threestudio/utils/GAN/loss.py @@ -0,0 +1,35 @@ +import torch +import torch.nn.functional as F + + +def generator_loss(discriminator, inputs, reconstructions, cond=None): + if cond is None: + logits_fake = discriminator(reconstructions.contiguous()) + else: + logits_fake = discriminator( + torch.cat((reconstructions.contiguous(), cond), dim=1) + ) + g_loss = -torch.mean(logits_fake) + return g_loss + + +def hinge_d_loss(logits_real, logits_fake): + loss_real = torch.mean(F.relu(1.0 - logits_real)) + loss_fake = torch.mean(F.relu(1.0 + logits_fake)) + d_loss = 0.5 * (loss_real + loss_fake) + return d_loss + + +def discriminator_loss(discriminator, inputs, reconstructions, cond=None): + if cond is None: + logits_real = discriminator(inputs.contiguous().detach()) + logits_fake = discriminator(reconstructions.contiguous().detach()) + else: + logits_real = discriminator( + torch.cat((inputs.contiguous().detach(), cond), dim=1) + ) + logits_fake = discriminator( + torch.cat((reconstructions.contiguous().detach(), cond), dim=1) + ) + d_loss = hinge_d_loss(logits_real, logits_fake).mean() + return d_loss diff --git a/threestudio/utils/GAN/mobilenet.py b/threestudio/utils/GAN/mobilenet.py new file mode 100644 index 0000000..1f5f8ef --- /dev/null +++ b/threestudio/utils/GAN/mobilenet.py @@ -0,0 +1,254 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +__all__ = ["MobileNetV3", "mobilenetv3"] + + +def conv_bn( + inp, + oup, + stride, + conv_layer=nn.Conv2d, + norm_layer=nn.BatchNorm2d, + nlin_layer=nn.ReLU, +): + return nn.Sequential( + conv_layer(inp, oup, 3, stride, 1, bias=False), + norm_layer(oup), + nlin_layer(inplace=True), + ) + + +def conv_1x1_bn( + inp, oup, conv_layer=nn.Conv2d, norm_layer=nn.BatchNorm2d, nlin_layer=nn.ReLU +): + return nn.Sequential( + conv_layer(inp, oup, 1, 1, 0, bias=False), + norm_layer(oup), + nlin_layer(inplace=True), + ) + + +class Hswish(nn.Module): + def __init__(self, inplace=True): + super(Hswish, self).__init__() + self.inplace = inplace + + def forward(self, x): + return x * F.relu6(x + 3.0, inplace=self.inplace) / 6.0 + + +class Hsigmoid(nn.Module): + def __init__(self, inplace=True): + super(Hsigmoid, self).__init__() + self.inplace = inplace + + def forward(self, x): + return F.relu6(x + 3.0, inplace=self.inplace) / 6.0 + + +class SEModule(nn.Module): + def __init__(self, channel, reduction=4): + super(SEModule, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction, bias=False), + nn.ReLU(inplace=True), + nn.Linear(channel // reduction, channel, bias=False), + Hsigmoid() + # nn.Sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y.expand_as(x) + + +class Identity(nn.Module): + def __init__(self, channel): + super(Identity, self).__init__() + + def forward(self, x): + return x + + +def make_divisible(x, divisible_by=8): + import numpy as np + + return int(np.ceil(x * 1.0 / divisible_by) * divisible_by) + + +class MobileBottleneck(nn.Module): + def __init__(self, inp, oup, kernel, stride, exp, se=False, nl="RE"): + super(MobileBottleneck, self).__init__() + assert stride in [1, 2] + assert kernel in [3, 5] + padding = (kernel - 1) // 2 + self.use_res_connect = stride == 1 and inp == oup + + conv_layer = nn.Conv2d + norm_layer = nn.BatchNorm2d + if nl == "RE": + nlin_layer = nn.ReLU # or ReLU6 + elif nl == "HS": + nlin_layer = Hswish + else: + raise NotImplementedError + if se: + SELayer = SEModule + else: + SELayer = Identity + + self.conv = nn.Sequential( + # pw + conv_layer(inp, exp, 1, 1, 0, bias=False), + norm_layer(exp), + nlin_layer(inplace=True), + # dw + conv_layer(exp, exp, kernel, stride, padding, groups=exp, bias=False), + norm_layer(exp), + SELayer(exp), + nlin_layer(inplace=True), + # pw-linear + conv_layer(exp, oup, 1, 1, 0, bias=False), + norm_layer(oup), + ) + + def forward(self, x): + if self.use_res_connect: + return x + self.conv(x) + else: + return self.conv(x) + + +class MobileNetV3(nn.Module): + def __init__( + self, n_class=1000, input_size=224, dropout=0.0, mode="small", width_mult=1.0 + ): + super(MobileNetV3, self).__init__() + input_channel = 16 + last_channel = 1280 + if mode == "large": + # refer to Table 1 in paper + mobile_setting = [ + # k, exp, c, se, nl, s, + [3, 16, 16, False, "RE", 1], + [3, 64, 24, False, "RE", 2], + [3, 72, 24, False, "RE", 1], + [5, 72, 40, True, "RE", 2], + [5, 120, 40, True, "RE", 1], + [5, 120, 40, True, "RE", 1], + [3, 240, 80, False, "HS", 2], + [3, 200, 80, False, "HS", 1], + [3, 184, 80, False, "HS", 1], + [3, 184, 80, False, "HS", 1], + [3, 480, 112, True, "HS", 1], + [3, 672, 112, True, "HS", 1], + [5, 672, 160, True, "HS", 2], + [5, 960, 160, True, "HS", 1], + [5, 960, 160, True, "HS", 1], + ] + elif mode == "small": + # refer to Table 2 in paper + mobile_setting = [ + # k, exp, c, se, nl, s, + [3, 16, 16, True, "RE", 2], + [3, 72, 24, False, "RE", 2], + [3, 88, 24, False, "RE", 1], + [5, 96, 40, True, "HS", 2], + [5, 240, 40, True, "HS", 1], + [5, 240, 40, True, "HS", 1], + [5, 120, 48, True, "HS", 1], + [5, 144, 48, True, "HS", 1], + [5, 288, 96, True, "HS", 2], + [5, 576, 96, True, "HS", 1], + [5, 576, 96, True, "HS", 1], + ] + else: + raise NotImplementedError + + # building first layer + assert input_size % 32 == 0 + last_channel = ( + make_divisible(last_channel * width_mult) + if width_mult > 1.0 + else last_channel + ) + self.features = [conv_bn(3, input_channel, 2, nlin_layer=Hswish)] + self.classifier = [] + + # building mobile blocks + for k, exp, c, se, nl, s in mobile_setting: + output_channel = make_divisible(c * width_mult) + exp_channel = make_divisible(exp * width_mult) + self.features.append( + MobileBottleneck( + input_channel, output_channel, k, s, exp_channel, se, nl + ) + ) + input_channel = output_channel + + # building last several layers + if mode == "large": + last_conv = make_divisible(960 * width_mult) + self.features.append( + conv_1x1_bn(input_channel, last_conv, nlin_layer=Hswish) + ) + self.features.append(nn.AdaptiveAvgPool2d(1)) + self.features.append(nn.Conv2d(last_conv, last_channel, 1, 1, 0)) + self.features.append(Hswish(inplace=True)) + elif mode == "small": + last_conv = make_divisible(576 * width_mult) + self.features.append( + conv_1x1_bn(input_channel, last_conv, nlin_layer=Hswish) + ) + # self.features.append(SEModule(last_conv)) # refer to paper Table2, but I think this is a mistake + self.features.append(nn.AdaptiveAvgPool2d(1)) + self.features.append(nn.Conv2d(last_conv, last_channel, 1, 1, 0)) + self.features.append(Hswish(inplace=True)) + else: + raise NotImplementedError + + # make it nn.Sequential + self.features = nn.Sequential(*self.features) + + # building classifier + self.classifier = nn.Sequential( + nn.Dropout(p=dropout), # refer to paper section 6 + nn.Linear(last_channel, n_class), + ) + + self._initialize_weights() + + def forward(self, x): + x = self.features(x) + x = x.mean(3).mean(2) + x = self.classifier(x) + return x + + def _initialize_weights(self): + # weight initialization + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out") + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.BatchNorm2d): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + if m.bias is not None: + nn.init.zeros_(m.bias) + + +def mobilenetv3(pretrained=False, **kwargs): + model = MobileNetV3(**kwargs) + if pretrained: + state_dict = torch.load("mobilenetv3_small_67.4.pth.tar") + model.load_state_dict(state_dict, strict=True) + # raise NotImplementedError + return model diff --git a/threestudio/utils/GAN/network_util.py b/threestudio/utils/GAN/network_util.py new file mode 100644 index 0000000..671df1b --- /dev/null +++ b/threestudio/utils/GAN/network_util.py @@ -0,0 +1,296 @@ +# adopted from +# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py +# and +# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +# and +# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py +# +# thanks! + + +import math +import os + +import numpy as np +import torch +import torch.nn as nn +from einops import repeat + +from threestudio.utils.GAN.util import instantiate_from_config + + +def make_beta_schedule( + schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3 +): + if schedule == "linear": + betas = ( + torch.linspace( + linear_start**0.5, linear_end**0.5, n_timestep, dtype=torch.float64 + ) + ** 2 + ) + + elif schedule == "cosine": + timesteps = ( + torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s + ) + alphas = timesteps / (1 + cosine_s) * np.pi / 2 + alphas = torch.cos(alphas).pow(2) + alphas = alphas / alphas[0] + betas = 1 - alphas[1:] / alphas[:-1] + betas = np.clip(betas, a_min=0, a_max=0.999) + + elif schedule == "sqrt_linear": + betas = torch.linspace( + linear_start, linear_end, n_timestep, dtype=torch.float64 + ) + elif schedule == "sqrt": + betas = ( + torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) + ** 0.5 + ) + else: + raise ValueError(f"schedule '{schedule}' unknown.") + return betas.numpy() + + +def make_ddim_timesteps( + ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True +): + if ddim_discr_method == "uniform": + c = num_ddpm_timesteps // num_ddim_timesteps + ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) + elif ddim_discr_method == "quad": + ddim_timesteps = ( + (np.linspace(0, np.sqrt(num_ddpm_timesteps * 0.8), num_ddim_timesteps)) ** 2 + ).astype(int) + else: + raise NotImplementedError( + f'There is no ddim discretization method called "{ddim_discr_method}"' + ) + + # assert ddim_timesteps.shape[0] == num_ddim_timesteps + # add one to get the final alpha values right (the ones from first scale to data during sampling) + steps_out = ddim_timesteps + 1 + if verbose: + print(f"Selected timesteps for ddim sampler: {steps_out}") + return steps_out + + +def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): + # select alphas for computing the variance schedule + alphas = alphacums[ddim_timesteps] + alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) + + # according the the formula provided in https://arxiv.org/abs/2010.02502 + sigmas = eta * np.sqrt( + (1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev) + ) + if verbose: + print( + f"Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}" + ) + print( + f"For the chosen value of eta, which is {eta}, " + f"this results in the following sigma_t schedule for ddim sampler {sigmas}" + ) + return sigmas, alphas, alphas_prev + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, + which defines the cumulative product of (1-beta) over time from t = [0,1]. + :param num_diffusion_timesteps: the number of betas to produce. + :param alpha_bar: a lambda that takes an argument t from 0 to 1 and + produces the cumulative product of (1-beta) up to that + part of the diffusion process. + :param max_beta: the maximum beta to use; use values lower than 1 to + prevent singularities. + """ + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + return np.array(betas) + + +def extract_into_tensor(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def checkpoint(func, inputs, params, flag): + """ + Evaluate a function without caching intermediate activations, allowing for + reduced memory at the expense of extra compute in the backward pass. + :param func: the function to evaluate. + :param inputs: the argument sequence to pass to `func`. + :param params: a sequence of parameters `func` depends on but does not + explicitly take as arguments. + :param flag: if False, disable gradient checkpointing. + """ + if flag: + args = tuple(inputs) + tuple(params) + return CheckpointFunction.apply(func, len(inputs), *args) + else: + return func(*inputs) + + +class CheckpointFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, run_function, length, *args): + ctx.run_function = run_function + ctx.input_tensors = list(args[:length]) + ctx.input_params = list(args[length:]) + + with torch.no_grad(): + output_tensors = ctx.run_function(*ctx.input_tensors) + return output_tensors + + @staticmethod + def backward(ctx, *output_grads): + ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] + with torch.enable_grad(): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) + input_grads = torch.autograd.grad( + output_tensors, + ctx.input_tensors + ctx.input_params, + output_grads, + allow_unused=True, + ) + del ctx.input_tensors + del ctx.input_params + del output_tensors + return (None, None) + input_grads + + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=timesteps.device) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + else: + embedding = repeat(timesteps, "b -> b d", d=dim) + return embedding + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def scale_module(module, scale): + """ + Scale the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def normalization(channels): + """ + Make a standard normalization layer. + :param channels: number of input channels. + :return: an nn.Module for normalization. + """ + return GroupNorm32(32, channels) + + +# PyTorch 1.7 has SiLU, but we support PyTorch 1.5. +class SiLU(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + + +class GroupNorm32(nn.GroupNorm): + def forward(self, x): + return super().forward(x.float()).type(x.dtype) + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def linear(*args, **kwargs): + """ + Create a linear module. + """ + return nn.Linear(*args, **kwargs) + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +class HybridConditioner(nn.Module): + def __init__(self, c_concat_config, c_crossattn_config): + super().__init__() + self.concat_conditioner = instantiate_from_config(c_concat_config) + self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) + + def forward(self, c_concat, c_crossattn): + c_concat = self.concat_conditioner(c_concat) + c_crossattn = self.crossattn_conditioner(c_crossattn) + return {"c_concat": [c_concat], "c_crossattn": [c_crossattn]} + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat( + shape[0], *((1,) * (len(shape) - 1)) + ) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() diff --git a/threestudio/utils/GAN/util.py b/threestudio/utils/GAN/util.py new file mode 100644 index 0000000..33a86d6 --- /dev/null +++ b/threestudio/utils/GAN/util.py @@ -0,0 +1,208 @@ +import importlib +import multiprocessing as mp +from collections import abc +from functools import partial +from inspect import isfunction +from queue import Queue +from threading import Thread + +import numpy as np +import torch +from einops import rearrange +from PIL import Image, ImageDraw, ImageFont + + +def log_txt_as_img(wh, xc, size=10): + # wh a tuple of (width, height) + # xc a list of captions to plot + b = len(xc) + txts = list() + for bi in range(b): + txt = Image.new("RGB", wh, color="white") + draw = ImageDraw.Draw(txt) + font = ImageFont.truetype("data/DejaVuSans.ttf", size=size) + nc = int(40 * (wh[0] / 256)) + lines = "\n".join( + xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc) + ) + + try: + draw.text((0, 0), lines, fill="black", font=font) + except UnicodeEncodeError: + print("Cant encode string for logging. Skipping.") + + txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 + txts.append(txt) + txts = np.stack(txts) + txts = torch.tensor(txts) + return txts + + +def ismap(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] > 3) + + +def isimage(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) + + +def exists(x): + return x is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def mean_flat(tensor): + """ + https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def count_params(model, verbose=False): + total_params = sum(p.numel() for p in model.parameters()) + if verbose: + print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.") + return total_params + + +def instantiate_from_config(config): + if not "target" in config: + if config == "__is_first_stage__": + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False): + # create dummy dataset instance + + # run prefetching + if idx_to_fn: + res = func(data, worker_id=idx) + else: + res = func(data) + Q.put([idx, res]) + Q.put("Done") + + +def parallel_data_prefetch( + func: callable, + data, + n_proc, + target_data_type="ndarray", + cpu_intensive=True, + use_worker_id=False, +): + # if target_data_type not in ["ndarray", "list"]: + # raise ValueError( + # "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray." + # ) + if isinstance(data, np.ndarray) and target_data_type == "list": + raise ValueError("list expected but function got ndarray.") + elif isinstance(data, abc.Iterable): + if isinstance(data, dict): + print( + f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.' + ) + data = list(data.values()) + if target_data_type == "ndarray": + data = np.asarray(data) + else: + data = list(data) + else: + raise TypeError( + f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}." + ) + + if cpu_intensive: + Q = mp.Queue(1000) + proc = mp.Process + else: + Q = Queue(1000) + proc = Thread + # spawn processes + if target_data_type == "ndarray": + arguments = [ + [func, Q, part, i, use_worker_id] + for i, part in enumerate(np.array_split(data, n_proc)) + ] + else: + step = ( + int(len(data) / n_proc + 1) + if len(data) % n_proc != 0 + else int(len(data) / n_proc) + ) + arguments = [ + [func, Q, part, i, use_worker_id] + for i, part in enumerate( + [data[i : i + step] for i in range(0, len(data), step)] + ) + ] + processes = [] + for i in range(n_proc): + p = proc(target=_do_parallel_data_prefetch, args=arguments[i]) + processes += [p] + + # start processes + print(f"Start prefetching...") + import time + + start = time.time() + gather_res = [[] for _ in range(n_proc)] + try: + for p in processes: + p.start() + + k = 0 + while k < n_proc: + # get result + res = Q.get() + if res == "Done": + k += 1 + else: + gather_res[res[0]] = res[1] + + except Exception as e: + print("Exception: ", e) + for p in processes: + p.terminate() + + raise e + finally: + for p in processes: + p.join() + print(f"Prefetching complete. [{time.time() - start} sec.]") + + if target_data_type == "ndarray": + if not isinstance(gather_res[0], np.ndarray): + return np.concatenate([np.asarray(r) for r in gather_res], axis=0) + + # order outputs + return np.concatenate(gather_res, axis=0) + elif target_data_type == "list": + out = [] + for r in gather_res: + out.extend(r) + return out + else: + return gather_res diff --git a/threestudio/utils/GAN/vae.py b/threestudio/utils/GAN/vae.py new file mode 100644 index 0000000..2b4741f --- /dev/null +++ b/threestudio/utils/GAN/vae.py @@ -0,0 +1,1028 @@ +# pytorch_diffusion + derived encoder decoder +import math + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + +from threestudio.utils.GAN.attention import LinearAttention +from threestudio.utils.GAN.util import instantiate_from_config + + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) + return emb + + +def nonlinearity(x): + # swish + return x * torch.sigmoid(x) + + +def Normalize(in_channels, num_groups=32): + return torch.nn.BatchNorm2d(num_features=in_channels) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=2, padding=0 + ) + + def forward(self, x): + if self.with_conv: + pad = (0, 1, 0, 1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__( + self, + *, + in_channels, + out_channels=None, + conv_shortcut=False, + dropout, + temb_channels=512, + ): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + else: + self.nin_shortcut = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x + h + + +class LinAttnBlock(LinearAttention): + """to match AttnBlock usage""" + + def __init__(self, in_channels): + super().__init__(dim=in_channels, heads=1, dim_head=in_channels) + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = q.reshape(b, c, h * w) + q = q.permute(0, 2, 1) # b,hw,c + k = k.reshape(b, c, h * w) # b,c,hw + w_ = torch.bmm(q, k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] + w_ = w_ * (int(c) ** (-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b, c, h * w) + w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q) + h_ = torch.bmm(v, w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] + h_ = h_.reshape(b, c, h, w) + + h_ = self.proj_out(h_) + + return x + h_ + + +def make_attn(in_channels, attn_type="vanilla"): + assert attn_type in ["vanilla", "linear", "none"], f"attn_type {attn_type} unknown" + print(f"making attention of type '{attn_type}' with {in_channels} in_channels") + if attn_type == "vanilla": + return AttnBlock(in_channels) + elif attn_type == "none": + return nn.Identity(in_channels) + else: + return LinAttnBlock(in_channels) + + +class Model(nn.Module): + def __init__( + self, + *, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks, + attn_resolutions, + dropout=0.0, + resamp_with_conv=True, + in_channels, + resolution, + use_timestep=True, + use_linear_attn=False, + attn_type="vanilla", + ): + super().__init__() + if use_linear_attn: + attn_type = "linear" + self.ch = ch + self.temb_ch = self.ch * 4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList( + [ + torch.nn.Linear(self.ch, self.temb_ch), + torch.nn.Linear(self.temb_ch, self.temb_ch), + ] + ) + + # downsampling + self.conv_in = torch.nn.Conv2d( + in_channels, self.ch, kernel_size=3, stride=1, padding=1 + ) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + skip_in = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + if i_block == self.num_res_blocks: + skip_in = ch * in_ch_mult[i_level] + block.append( + ResnetBlock( + in_channels=block_in + skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, out_ch, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x, t=None, context=None): + # assert x.shape[2] == x.shape[3] == self.resolution + if context is not None: + # assume aligned context, cat along channel axis + x = torch.cat((x, context), dim=1) + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block]( + torch.cat([h, hs.pop()], dim=1), temb + ) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + def get_last_layer(self): + return self.conv_out.weight + + +class Encoder(nn.Module): + def __init__( + self, + *, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks, + attn_resolutions, + dropout=0.0, + resamp_with_conv=True, + in_channels, + resolution, + z_channels, + double_z=True, + use_linear_attn=False, + attn_type="vanilla", + **ignore_kwargs, + ): + super().__init__() + if use_linear_attn: + attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.attn_resolutions = attn_resolutions + + # downsampling + self.conv_in = torch.nn.Conv2d( + in_channels, self.ch, kernel_size=3, stride=1, padding=1 + ) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + if len(attn_resolutions) > 0: + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, + 2 * z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1, + ) + + def forward(self, x): + # timestep embedding + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + if len(self.attn_resolutions) > 0: + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__( + self, + *, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks, + attn_resolutions, + dropout=0.0, + resamp_with_conv=True, + in_channels, + resolution, + z_channels, + give_pre_end=False, + tanh_out=False, + use_linear_attn=False, + attn_type="vanilla", + **ignorekwargs, + ): + super().__init__() + if use_linear_attn: + attn_type = "linear" + self.ch = ch + # self.temb_ch = 3 + self.temb_ch = 64 + # self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.tanh_out = tanh_out + self.attn_resolutions = attn_resolutions + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,) + tuple(ch_mult) + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + print( + "Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape) + ) + ) + + # z to block_in + self.conv_in = torch.nn.Conv2d( + z_channels, block_in, kernel_size=3, stride=1, padding=1 + ) + + self.conv_in3 = torch.nn.Conv2d( + z_channels + 3, block_in, kernel_size=3, stride=1, padding=1 + ) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + up.rgb_conv = torch.nn.Conv2d( + block_in + 3, 3, kernel_size=3, stride=1, padding=1 + ) + up.rgb_cat_conv = torch.nn.Conv2d( + block_in + 3, block_in, kernel_size=3, stride=1, padding=1 + ) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, out_ch, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, z, temb=None): + # assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + # temb = None + + # z to block_in + rgb = z[:, :3] + if z.shape[1] == self.z_shape[1] + 3: + h = self.conv_in3(z) + else: + h = self.conv_in(z) + + # middle + # h = self.mid.block_1(h, temb) + # h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + + rgb = torch.nn.functional.interpolate(rgb, scale_factor=4.0, mode="bilinear") + rgb = torch.sigmoid(torch.logit(rgb, eps=1e-3) + h) + return rgb + + +class SimpleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, *args, **kwargs): + super().__init__() + self.model = nn.ModuleList( + [ + nn.Conv2d(in_channels, in_channels, 1), + ResnetBlock( + in_channels=in_channels, + out_channels=2 * in_channels, + temb_channels=0, + dropout=0.0, + ), + ResnetBlock( + in_channels=2 * in_channels, + out_channels=4 * in_channels, + temb_channels=0, + dropout=0.0, + ), + ResnetBlock( + in_channels=4 * in_channels, + out_channels=2 * in_channels, + temb_channels=0, + dropout=0.0, + ), + nn.Conv2d(2 * in_channels, in_channels, 1), + Upsample(in_channels, with_conv=True), + ] + ) + # end + self.norm_out = Normalize(in_channels) + self.conv_out = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x): + for i, layer in enumerate(self.model): + if i in [1, 2, 3]: + x = layer(x, None) + else: + x = layer(x) + + h = self.norm_out(x) + h = nonlinearity(h) + x = self.conv_out(h) + return x + + +class UpsampleDecoder(nn.Module): + def __init__( + self, + in_channels, + out_channels, + ch, + num_res_blocks, + resolution, + ch_mult=(2, 2), + dropout=0.0, + ): + super().__init__() + # upsampling + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + block_in = in_channels + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.upsample_blocks = nn.ModuleList() + self.rgb_blocks = nn.ModuleList() + for i_level in range(self.num_resolutions): + res_block = [] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + res_block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + self.res_blocks.append(nn.ModuleList(res_block)) + if i_level != self.num_resolutions - 1: + self.upsample_blocks.append(Upsample(block_in, True)) + curr_res = curr_res * 2 + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, out_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x): + # upsampling + h = x + for k, i_level in enumerate(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.res_blocks[i_level][i_block](h, None) + if i_level != self.num_resolutions - 1: + h = self.upsample_blocks[k](h) + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class LatentRescaler(nn.Module): + def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): + super().__init__() + # residual block, interpolate, residual block + self.factor = factor + self.conv_in = nn.Conv2d( + in_channels, mid_channels, kernel_size=3, stride=1, padding=1 + ) + self.res_block1 = nn.ModuleList( + [ + ResnetBlock( + in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0, + ) + for _ in range(depth) + ] + ) + self.attn = AttnBlock(mid_channels) + self.res_block2 = nn.ModuleList( + [ + ResnetBlock( + in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0, + ) + for _ in range(depth) + ] + ) + + self.conv_out = nn.Conv2d( + mid_channels, + out_channels, + kernel_size=1, + ) + + def forward(self, x): + x = self.conv_in(x) + for block in self.res_block1: + x = block(x, None) + x = torch.nn.functional.interpolate( + x, + size=( + int(round(x.shape[2] * self.factor)), + int(round(x.shape[3] * self.factor)), + ), + ) + x = self.attn(x) + for block in self.res_block2: + x = block(x, None) + x = self.conv_out(x) + return x + + +class MergedRescaleEncoder(nn.Module): + def __init__( + self, + in_channels, + ch, + resolution, + out_ch, + num_res_blocks, + attn_resolutions, + dropout=0.0, + resamp_with_conv=True, + ch_mult=(1, 2, 4, 8), + rescale_factor=1.0, + rescale_module_depth=1, + ): + super().__init__() + intermediate_chn = ch * ch_mult[-1] + self.encoder = Encoder( + in_channels=in_channels, + num_res_blocks=num_res_blocks, + ch=ch, + ch_mult=ch_mult, + z_channels=intermediate_chn, + double_z=False, + resolution=resolution, + attn_resolutions=attn_resolutions, + dropout=dropout, + resamp_with_conv=resamp_with_conv, + out_ch=None, + ) + self.rescaler = LatentRescaler( + factor=rescale_factor, + in_channels=intermediate_chn, + mid_channels=intermediate_chn, + out_channels=out_ch, + depth=rescale_module_depth, + ) + + def forward(self, x): + x = self.encoder(x) + x = self.rescaler(x) + return x + + +class MergedRescaleDecoder(nn.Module): + def __init__( + self, + z_channels, + out_ch, + resolution, + num_res_blocks, + attn_resolutions, + ch, + ch_mult=(1, 2, 4, 8), + dropout=0.0, + resamp_with_conv=True, + rescale_factor=1.0, + rescale_module_depth=1, + ): + super().__init__() + tmp_chn = z_channels * ch_mult[-1] + self.decoder = Decoder( + out_ch=out_ch, + z_channels=tmp_chn, + attn_resolutions=attn_resolutions, + dropout=dropout, + resamp_with_conv=resamp_with_conv, + in_channels=None, + num_res_blocks=num_res_blocks, + ch_mult=ch_mult, + resolution=resolution, + ch=ch, + ) + self.rescaler = LatentRescaler( + factor=rescale_factor, + in_channels=z_channels, + mid_channels=tmp_chn, + out_channels=tmp_chn, + depth=rescale_module_depth, + ) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Upsampler(nn.Module): + def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): + super().__init__() + assert out_size >= in_size + num_blocks = int(np.log2(out_size // in_size)) + 1 + factor_up = 1.0 + (out_size % in_size) + print( + f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}" + ) + self.rescaler = LatentRescaler( + factor=factor_up, + in_channels=in_channels, + mid_channels=2 * in_channels, + out_channels=in_channels, + ) + self.decoder = Decoder( + out_ch=out_channels, + resolution=out_size, + z_channels=in_channels, + num_res_blocks=2, + attn_resolutions=[], + in_channels=None, + ch=in_channels, + ch_mult=[ch_mult for _ in range(num_blocks)], + ) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Resize(nn.Module): + def __init__(self, in_channels=None, learned=False, mode="bilinear"): + super().__init__() + self.with_conv = learned + self.mode = mode + if self.with_conv: + print( + f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode" + ) + raise NotImplementedError() + assert in_channels is not None + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=4, stride=2, padding=1 + ) + + def forward(self, x, scale_factor=1.0): + if scale_factor == 1.0: + return x + else: + x = torch.nn.functional.interpolate( + x, mode=self.mode, align_corners=False, scale_factor=scale_factor + ) + return x + + +class FirstStagePostProcessor(nn.Module): + def __init__( + self, + ch_mult: list, + in_channels, + pretrained_model: nn.Module = None, + reshape=False, + n_channels=None, + dropout=0.0, + pretrained_config=None, + ): + super().__init__() + if pretrained_config is None: + assert ( + pretrained_model is not None + ), 'Either "pretrained_model" or "pretrained_config" must not be None' + self.pretrained_model = pretrained_model + else: + assert ( + pretrained_config is not None + ), 'Either "pretrained_model" or "pretrained_config" must not be None' + self.instantiate_pretrained(pretrained_config) + + self.do_reshape = reshape + + if n_channels is None: + n_channels = self.pretrained_model.encoder.ch + + self.proj_norm = Normalize(in_channels, num_groups=in_channels // 2) + self.proj = nn.Conv2d( + in_channels, n_channels, kernel_size=3, stride=1, padding=1 + ) + + blocks = [] + downs = [] + ch_in = n_channels + for m in ch_mult: + blocks.append( + ResnetBlock( + in_channels=ch_in, out_channels=m * n_channels, dropout=dropout + ) + ) + ch_in = m * n_channels + downs.append(Downsample(ch_in, with_conv=False)) + + self.model = nn.ModuleList(blocks) + self.downsampler = nn.ModuleList(downs) + + def instantiate_pretrained(self, config): + model = instantiate_from_config(config) + self.pretrained_model = model.eval() + # self.pretrained_model.train = False + for param in self.pretrained_model.parameters(): + param.requires_grad = False + + @torch.no_grad() + def encode_with_pretrained(self, x): + c = self.pretrained_model.encode(x) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + return c + + def forward(self, x): + z_fs = self.encode_with_pretrained(x) + z = self.proj_norm(z_fs) + z = self.proj(z) + z = nonlinearity(z) + + for submodel, downmodel in zip(self.model, self.downsampler): + z = submodel(z, temb=None) + z = downmodel(z) + + if self.do_reshape: + z = rearrange(z, "b c h w -> b (h w) c") + return z diff --git a/threestudio/utils/__init__.py b/threestudio/utils/__init__.py new file mode 100644 index 0000000..0e44449 --- /dev/null +++ b/threestudio/utils/__init__.py @@ -0,0 +1 @@ +from . import base diff --git a/threestudio/utils/base.py b/threestudio/utils/base.py new file mode 100644 index 0000000..97f1f66 --- /dev/null +++ b/threestudio/utils/base.py @@ -0,0 +1,118 @@ +from dataclasses import dataclass + +import torch +import torch.nn as nn + +from threestudio.utils.config import parse_structured +from threestudio.utils.misc import get_device, load_module_weights +from threestudio.utils.typing import * + + +class Configurable: + @dataclass + class Config: + pass + + def __init__(self, cfg: Optional[dict] = None) -> None: + super().__init__() + self.cfg = parse_structured(self.Config, cfg) + + +class Updateable: + def do_update_step( + self, epoch: int, global_step: int, on_load_weights: bool = False + ): + for attr in self.__dir__(): + if attr.startswith("_"): + continue + try: + module = getattr(self, attr) + except: + continue # ignore attributes like property, which can't be retrived using getattr? + if isinstance(module, Updateable): + module.do_update_step( + epoch, global_step, on_load_weights=on_load_weights + ) + self.update_step(epoch, global_step, on_load_weights=on_load_weights) + + def do_update_step_end(self, epoch: int, global_step: int): + for attr in self.__dir__(): + if attr.startswith("_"): + continue + try: + module = getattr(self, attr) + except: + continue # ignore attributes like property, which can't be retrived using getattr? + if isinstance(module, Updateable): + module.do_update_step_end(epoch, global_step) + self.update_step_end(epoch, global_step) + + def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False): + # override this method to implement custom update logic + # if on_load_weights is True, you should be careful doing things related to model evaluations, + # as the models and tensors are not guarenteed to be on the same device + pass + + def update_step_end(self, epoch: int, global_step: int): + pass + + +def update_if_possible(module: Any, epoch: int, global_step: int) -> None: + if isinstance(module, Updateable): + module.do_update_step(epoch, global_step) + + +def update_end_if_possible(module: Any, epoch: int, global_step: int) -> None: + if isinstance(module, Updateable): + module.do_update_step_end(epoch, global_step) + + +class BaseObject(Updateable): + @dataclass + class Config: + pass + + cfg: Config # add this to every subclass of BaseObject to enable static type checking + + def __init__( + self, cfg: Optional[Union[dict, DictConfig]] = None, *args, **kwargs + ) -> None: + super().__init__() + self.cfg = parse_structured(self.Config, cfg) + self.device = get_device() + self.configure(*args, **kwargs) + + def configure(self, *args, **kwargs) -> None: + pass + + +class BaseModule(nn.Module, Updateable): + @dataclass + class Config: + weights: Optional[str] = None + + cfg: Config # add this to every subclass of BaseModule to enable static type checking + + def __init__( + self, cfg: Optional[Union[dict, DictConfig]] = None, *args, **kwargs + ) -> None: + super().__init__() + self.cfg = parse_structured(self.Config, cfg) + self.device = get_device() + self.configure(*args, **kwargs) + if self.cfg.weights is not None: + # format: path/to/weights:module_name + weights_path, module_name = self.cfg.weights.split(":") + state_dict, epoch, global_step = load_module_weights( + weights_path, module_name=module_name, map_location="cpu" + ) + self.load_state_dict(state_dict) + self.do_update_step( + epoch, global_step, on_load_weights=True + ) # restore states + # dummy tensor to indicate model state + self._dummy: Float[Tensor, "..."] + self.register_buffer("_dummy", torch.zeros(0).float(), persistent=False) + + def configure(self, *args, **kwargs) -> None: + pass diff --git a/threestudio/utils/callbacks.py b/threestudio/utils/callbacks.py new file mode 100644 index 0000000..c6a765a --- /dev/null +++ b/threestudio/utils/callbacks.py @@ -0,0 +1,156 @@ +import os +import shutil +import subprocess + +import pytorch_lightning + +from threestudio.utils.config import dump_config +from threestudio.utils.misc import parse_version + +if parse_version(pytorch_lightning.__version__) > parse_version("1.8"): + from pytorch_lightning.callbacks import Callback +else: + from pytorch_lightning.callbacks.base import Callback + +from pytorch_lightning.callbacks.progress import TQDMProgressBar +from pytorch_lightning.utilities.rank_zero import rank_zero_only, rank_zero_warn + + +class VersionedCallback(Callback): + def __init__(self, save_root, version=None, use_version=True): + self.save_root = save_root + self._version = version + self.use_version = use_version + + @property + def version(self) -> int: + """Get the experiment version. + + Returns: + The experiment version if specified else the next version. + """ + if self._version is None: + self._version = self._get_next_version() + return self._version + + def _get_next_version(self): + existing_versions = [] + if os.path.isdir(self.save_root): + for f in os.listdir(self.save_root): + bn = os.path.basename(f) + if bn.startswith("version_"): + dir_ver = os.path.splitext(bn)[0].split("_")[1].replace("/", "") + existing_versions.append(int(dir_ver)) + if len(existing_versions) == 0: + return 0 + return max(existing_versions) + 1 + + @property + def savedir(self): + if not self.use_version: + return self.save_root + return os.path.join( + self.save_root, + self.version + if isinstance(self.version, str) + else f"version_{self.version}", + ) + + +class CodeSnapshotCallback(VersionedCallback): + def __init__(self, save_root, version=None, use_version=True): + super().__init__(save_root, version, use_version) + + def get_file_list(self): + return [ + b.decode() + for b in set( + subprocess.check_output( + 'git ls-files -- ":!:load/*"', shell=True + ).splitlines() + ) + | set( # hard code, TODO: use config to exclude folders or files + subprocess.check_output( + "git ls-files --others --exclude-standard", shell=True + ).splitlines() + ) + ] + + @rank_zero_only + def save_code_snapshot(self): + os.makedirs(self.savedir, exist_ok=True) + for f in self.get_file_list(): + if not os.path.exists(f) or os.path.isdir(f): + continue + os.makedirs(os.path.join(self.savedir, os.path.dirname(f)), exist_ok=True) + shutil.copyfile(f, os.path.join(self.savedir, f)) + + def on_fit_start(self, trainer, pl_module): + try: + self.save_code_snapshot() + except: + rank_zero_warn( + "Code snapshot is not saved. Please make sure you have git installed and are in a git repository." + ) + + +class ConfigSnapshotCallback(VersionedCallback): + def __init__(self, config_path, config, save_root, version=None, use_version=True): + super().__init__(save_root, version, use_version) + self.config_path = config_path + self.config = config + + @rank_zero_only + def save_config_snapshot(self): + os.makedirs(self.savedir, exist_ok=True) + dump_config(os.path.join(self.savedir, "parsed.yaml"), self.config) + shutil.copyfile(self.config_path, os.path.join(self.savedir, "raw.yaml")) + + def on_fit_start(self, trainer, pl_module): + self.save_config_snapshot() + + +class CustomProgressBar(TQDMProgressBar): + def get_metrics(self, *args, **kwargs): + # don't show the version number + items = super().get_metrics(*args, **kwargs) + items.pop("v_num", None) + return items + + +class ProgressCallback(Callback): + def __init__(self, save_path): + super().__init__() + self.save_path = save_path + self._file_handle = None + + @property + def file_handle(self): + if self._file_handle is None: + self._file_handle = open(self.save_path, "w") + return self._file_handle + + @rank_zero_only + def write(self, msg: str) -> None: + self.file_handle.seek(0) + self.file_handle.truncate() + self.file_handle.write(msg) + self.file_handle.flush() + + @rank_zero_only + def on_train_batch_end(self, trainer, pl_module, *args, **kwargs): + self.write( + f"Generation progress: {pl_module.true_global_step / trainer.max_steps * 100:.2f}%" + ) + + @rank_zero_only + def on_validation_start(self, trainer, pl_module): + self.write(f"Rendering validation image ...") + + @rank_zero_only + def on_test_start(self, trainer, pl_module): + self.write(f"Rendering video ...") + + @rank_zero_only + def on_predict_start(self, trainer, pl_module): + self.write(f"Exporting mesh assets ...") diff --git a/threestudio/utils/config.py b/threestudio/utils/config.py new file mode 100644 index 0000000..88a7d09 --- /dev/null +++ b/threestudio/utils/config.py @@ -0,0 +1,128 @@ +import os +from dataclasses import dataclass, field +from datetime import datetime + +from omegaconf import OmegaConf + +import threestudio +from threestudio.utils.typing import * + +# ============ Register OmegaConf Recolvers ============= # +OmegaConf.register_new_resolver( + "calc_exp_lr_decay_rate", lambda factor, n: factor ** (1.0 / n) +) +OmegaConf.register_new_resolver("add", lambda a, b: a + b) +OmegaConf.register_new_resolver("sub", lambda a, b: a - b) +OmegaConf.register_new_resolver("mul", lambda a, b: a * b) +OmegaConf.register_new_resolver("div", lambda a, b: a / b) +OmegaConf.register_new_resolver("idiv", lambda a, b: a // b) +OmegaConf.register_new_resolver("basename", lambda p: os.path.basename(p)) +OmegaConf.register_new_resolver("rmspace", lambda s, sub: s.replace(" ", sub)) +OmegaConf.register_new_resolver("tuple2", lambda s: [float(s), float(s)]) +OmegaConf.register_new_resolver("gt0", lambda s: s > 0) +OmegaConf.register_new_resolver("cmaxgt0", lambda s: C_max(s) > 0) +OmegaConf.register_new_resolver("not", lambda s: not s) +OmegaConf.register_new_resolver( + "cmaxgt0orcmaxgt0", lambda a, b: C_max(a) > 0 or C_max(b) > 0 +) +# ======================================================= # + + +def C_max(value: Any) -> float: + if isinstance(value, int) or isinstance(value, float): + pass + else: + value = config_to_primitive(value) + if not isinstance(value, list): + raise TypeError("Scalar specification only supports list, got", type(value)) + if len(value) >= 6: + max_value = value[2] + for i in range(4, len(value), 2): + max_value = max(max_value, value[i]) + value = [value[0], value[1], max_value, value[3]] + if len(value) == 3: + value = [0] + value + assert len(value) == 4 + start_step, start_value, end_value, end_step = value + value = max(start_value, end_value) + return value + + +@dataclass +class ExperimentConfig: + name: str = "default" + description: str = "" + tag: str = "" + seed: int = 0 + use_timestamp: bool = True + timestamp: Optional[str] = None + exp_root_dir: str = "outputs" + + ### these shouldn't be set manually + exp_dir: str = "outputs/default" + trial_name: str = "exp" + trial_dir: str = "outputs/default/exp" + n_gpus: int = 1 + ### + + resume: Optional[str] = None + + data_type: str = "" + data: dict = field(default_factory=dict) + + system_type: str = "" + system: dict = field(default_factory=dict) + + # accept pytorch-lightning trainer parameters + # see https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api + trainer: dict = field(default_factory=dict) + + # accept pytorch-lightning checkpoint callback parameters + # see https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.ModelCheckpoint.html#modelcheckpoint + checkpoint: dict = field(default_factory=dict) + + def __post_init__(self): + if not self.tag and not self.use_timestamp: + raise ValueError("Either tag is specified or use_timestamp is True.") + self.trial_name = self.tag + # if resume from an existing config, self.timestamp should not be None + if self.timestamp is None: + self.timestamp = "" + if self.use_timestamp: + if self.n_gpus > 1: + threestudio.warn( + "Timestamp is disabled when using multiple GPUs, please make sure you have a unique tag." + ) + else: + self.timestamp = datetime.now().strftime("@%Y%m%d-%H%M%S") + self.trial_name += self.timestamp + self.exp_dir = os.path.join(self.exp_root_dir, self.name) + self.trial_dir = os.path.join(self.exp_dir, self.trial_name) + os.makedirs(self.trial_dir, exist_ok=True) + + +def load_config(*yamls: str, cli_args: list = [], from_string=False, **kwargs) -> Any: + if from_string: + yaml_confs = [OmegaConf.create(s) for s in yamls] + else: + yaml_confs = [OmegaConf.load(f) for f in yamls] + cli_conf = OmegaConf.from_cli(cli_args) + cfg = OmegaConf.merge(*yaml_confs, cli_conf, kwargs) + OmegaConf.resolve(cfg) + assert isinstance(cfg, DictConfig) + scfg = parse_structured(ExperimentConfig, cfg) + return scfg + + +def config_to_primitive(config, resolve: bool = True) -> Any: + return OmegaConf.to_container(config, resolve=resolve) + + +def dump_config(path: str, config) -> None: + with open(path, "w") as fp: + OmegaConf.save(config=config, f=fp) + + +def parse_structured(fields: Any, cfg: Optional[Union[dict, DictConfig]] = None) -> Any: + scfg = OmegaConf.structured(fields(**cfg)) + return scfg diff --git a/threestudio/utils/loss.py b/threestudio/utils/loss.py new file mode 100644 index 0000000..eb0c725 --- /dev/null +++ b/threestudio/utils/loss.py @@ -0,0 +1,16 @@ +import torch + + +def _tensor_size(t): + return t.size()[1] * t.size()[2] * t.size()[3] + + +def tv_loss(x): + batch_size = x.size()[0] + h_x = x.size()[2] + w_x = x.size()[3] + count_h = _tensor_size(x[:, :, 1:, :]) + count_w = _tensor_size(x[:, :, :, 1:]) + h_tv = torch.pow((x[:, :, 1:, :] - x[:, :, : h_x - 1, :]), 2).sum() + w_tv = torch.pow((x[:, :, :, 1:] - x[:, :, :, : w_x - 1]), 2).sum() + return 2 * (h_tv / count_h + w_tv / count_w) / batch_size diff --git a/threestudio/utils/misc.py b/threestudio/utils/misc.py new file mode 100644 index 0000000..f2378f5 --- /dev/null +++ b/threestudio/utils/misc.py @@ -0,0 +1,161 @@ +import gc +import math +import os +import re + +import tinycudann as tcnn +import torch +from packaging import version + +from threestudio.utils.config import config_to_primitive +from threestudio.utils.typing import * + + +def parse_version(ver: str): + return version.parse(ver) + + +def get_rank(): + # SLURM_PROCID can be set even if SLURM is not managing the multiprocessing, + # therefore LOCAL_RANK needs to be checked first + rank_keys = ("RANK", "LOCAL_RANK", "SLURM_PROCID", "JSM_NAMESPACE_RANK") + for key in rank_keys: + rank = os.environ.get(key) + if rank is not None: + return int(rank) + return 0 + + +def get_device(): + return torch.device(f"cuda:{get_rank()}") + + +def load_module_weights( + path, module_name=None, ignore_modules=None, map_location=None +) -> Tuple[dict, int, int]: + if module_name is not None and ignore_modules is not None: + raise ValueError("module_name and ignore_modules cannot be both set") + if map_location is None: + map_location = get_device() + + ckpt = torch.load(path, map_location=map_location) + state_dict = ckpt["state_dict"] + state_dict_to_load = state_dict + + if ignore_modules is not None: + state_dict_to_load = {} + for k, v in state_dict.items(): + ignore = any( + [k.startswith(ignore_module + ".") for ignore_module in ignore_modules] + ) + if ignore: + continue + state_dict_to_load[k] = v + + if module_name is not None: + state_dict_to_load = {} + for k, v in state_dict.items(): + m = re.match(rf"^{module_name}\.(.*)$", k) + if m is None: + continue + state_dict_to_load[m.group(1)] = v + + return state_dict_to_load, ckpt["epoch"], ckpt["global_step"] + + +def C(value: Any, epoch: int, global_step: int, interpolation="linear") -> float: + if isinstance(value, int) or isinstance(value, float): + pass + else: + value = config_to_primitive(value) + if not isinstance(value, list): + raise TypeError("Scalar specification only supports list, got", type(value)) + if len(value) == 3: + value = [0] + value + if len(value) >= 6: + select_i = 3 + for i in range(3, len(value) - 2, 2): + if global_step >= value[i]: + select_i = i + 2 + if select_i != 3: + start_value, start_step = value[select_i - 3], value[select_i - 2] + else: + start_step, start_value = value[:2] + end_value, end_step = value[select_i - 1], value[select_i] + value = [start_step, start_value, end_value, end_step] + assert len(value) == 4 + start_step, start_value, end_value, end_step = value + if isinstance(end_step, int): + current_step = global_step + elif isinstance(end_step, float): + current_step = epoch + t = max(min(1.0, (current_step - start_step) / (end_step - start_step)), 0.0) + if interpolation == "linear": + value = start_value + (end_value - start_value) * t + elif interpolation == "exp": + value = math.exp(math.log(start_value) * (1 - t) + math.log(end_value) * t) + else: + raise ValueError( + f"Unknown interpolation method: {interpolation}, only support linear and exp" + ) + return value + + +def cleanup(): + gc.collect() + torch.cuda.empty_cache() + tcnn.free_temporary_memory() + + +def finish_with_cleanup(func: Callable): + def wrapper(*args, **kwargs): + out = func(*args, **kwargs) + cleanup() + return out + + return wrapper + + +def _distributed_available(): + return torch.distributed.is_available() and torch.distributed.is_initialized() + + +def barrier(): + if not _distributed_available(): + return + else: + torch.distributed.barrier() + + +def broadcast(tensor, src=0): + if not _distributed_available(): + return tensor + else: + torch.distributed.broadcast(tensor, src=src) + return tensor + + +def enable_gradient(model, enabled: bool = True) -> None: + for param in model.parameters(): + param.requires_grad_(enabled) + + +def find_last_path(path: str): + if (path is not None) and ("LAST" in path): + path = path.replace(" ", "_") + base_dir_prefix, suffix = path.split("LAST", 1) + base_dir = os.path.dirname(base_dir_prefix) + prefix = os.path.split(base_dir_prefix)[-1] + base_dir_prefix = os.path.join(base_dir, prefix) + all_path = os.listdir(base_dir) + all_path = [os.path.join(base_dir, dir) for dir in all_path] + filtered_path = [dir for dir in all_path if dir.startswith(base_dir_prefix)] + filtered_path.sort(reverse=True) + last_path = filtered_path[0] + new_path = last_path + suffix + if os.path.exists(new_path): + return new_path + else: + raise FileNotFoundError(new_path) + else: + return path diff --git a/threestudio/utils/ops.py b/threestudio/utils/ops.py new file mode 100644 index 0000000..1e09e68 --- /dev/null +++ b/threestudio/utils/ops.py @@ -0,0 +1,571 @@ +import math +from collections import defaultdict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from igl import fast_winding_number_for_meshes, point_mesh_squared_distance, read_obj +from torch.autograd import Function +from torch.cuda.amp import custom_bwd, custom_fwd + +import threestudio +from threestudio.utils.typing import * + + +def dot(x, y): + return torch.sum(x * y, -1, keepdim=True) + + +def reflect(x, n): + return 2 * dot(x, n) * n - x + + +ValidScale = Union[Tuple[float, float], Num[Tensor, "2 D"]] + + +def scale_tensor( + dat: Num[Tensor, "... D"], inp_scale: ValidScale, tgt_scale: ValidScale +): + if inp_scale is None: + inp_scale = (0, 1) + if tgt_scale is None: + tgt_scale = (0, 1) + if isinstance(tgt_scale, Tensor): + assert dat.shape[-1] == tgt_scale.shape[-1] + dat = (dat - inp_scale[0]) / (inp_scale[1] - inp_scale[0]) + dat = dat * (tgt_scale[1] - tgt_scale[0]) + tgt_scale[0] + return dat + + +class _TruncExp(Function): # pylint: disable=abstract-method + # Implementation from torch-ngp: + # https://github.com/ashawkey/torch-ngp/blob/93b08a0d4ec1cc6e69d85df7f0acdfb99603b628/activation.py + @staticmethod + @custom_fwd(cast_inputs=torch.float32) + def forward(ctx, x): # pylint: disable=arguments-differ + ctx.save_for_backward(x) + return torch.exp(x) + + @staticmethod + @custom_bwd + def backward(ctx, g): # pylint: disable=arguments-differ + x = ctx.saved_tensors[0] + return g * torch.exp(torch.clamp(x, max=15)) + + +class SpecifyGradient(Function): + # Implementation from stable-dreamfusion + # https://github.com/ashawkey/stable-dreamfusion + @staticmethod + @custom_fwd + def forward(ctx, input_tensor, gt_grad): + ctx.save_for_backward(gt_grad) + # we return a dummy value 1, which will be scaled by amp's scaler so we get the scale in backward. + return torch.ones([1], device=input_tensor.device, dtype=input_tensor.dtype) + + @staticmethod + @custom_bwd + def backward(ctx, grad_scale): + (gt_grad,) = ctx.saved_tensors + gt_grad = gt_grad * grad_scale + return gt_grad, None + + +trunc_exp = _TruncExp.apply + + +def get_activation(name) -> Callable: + if name is None: + return lambda x: x + name = name.lower() + if name == "none": + return lambda x: x + elif name == "lin2srgb": + return lambda x: torch.where( + x > 0.0031308, + torch.pow(torch.clamp(x, min=0.0031308), 1.0 / 2.4) * 1.055 - 0.055, + 12.92 * x, + ).clamp(0.0, 1.0) + elif name == "exp": + return lambda x: torch.exp(x) + elif name == "shifted_exp": + return lambda x: torch.exp(x - 1.0) + elif name == "trunc_exp": + return trunc_exp + elif name == "shifted_trunc_exp": + return lambda x: trunc_exp(x - 1.0) + elif name == "sigmoid": + return lambda x: torch.sigmoid(x) + elif name == "tanh": + return lambda x: torch.tanh(x) + elif name == "shifted_softplus": + return lambda x: F.softplus(x - 1.0) + elif name == "scale_-11_01": + return lambda x: x * 0.5 + 0.5 + else: + try: + return getattr(F, name) + except AttributeError: + raise ValueError(f"Unknown activation function: {name}") + + +def chunk_batch(func: Callable, chunk_size: int, *args, **kwargs) -> Any: + if chunk_size <= 0: + return func(*args, **kwargs) + B = None + for arg in list(args) + list(kwargs.values()): + if isinstance(arg, torch.Tensor): + B = arg.shape[0] + break + assert ( + B is not None + ), "No tensor found in args or kwargs, cannot determine batch size." + out = defaultdict(list) + out_type = None + # max(1, B) to support B == 0 + for i in range(0, max(1, B), chunk_size): + out_chunk = func( + *[ + arg[i : i + chunk_size] if isinstance(arg, torch.Tensor) else arg + for arg in args + ], + **{ + k: arg[i : i + chunk_size] if isinstance(arg, torch.Tensor) else arg + for k, arg in kwargs.items() + }, + ) + if out_chunk is None: + continue + out_type = type(out_chunk) + if isinstance(out_chunk, torch.Tensor): + out_chunk = {0: out_chunk} + elif isinstance(out_chunk, tuple) or isinstance(out_chunk, list): + chunk_length = len(out_chunk) + out_chunk = {i: chunk for i, chunk in enumerate(out_chunk)} + elif isinstance(out_chunk, dict): + pass + else: + print( + f"Return value of func must be in type [torch.Tensor, list, tuple, dict], get {type(out_chunk)}." + ) + exit(1) + for k, v in out_chunk.items(): + v = v if torch.is_grad_enabled() else v.detach() + out[k].append(v) + + if out_type is None: + return None + + out_merged: Dict[Any, Optional[torch.Tensor]] = {} + for k, v in out.items(): + if all([vv is None for vv in v]): + # allow None in return value + out_merged[k] = None + elif all([isinstance(vv, torch.Tensor) for vv in v]): + out_merged[k] = torch.cat(v, dim=0) + else: + raise TypeError( + f"Unsupported types in return value of func: {[type(vv) for vv in v if not isinstance(vv, torch.Tensor)]}" + ) + + if out_type is torch.Tensor: + return out_merged[0] + elif out_type in [tuple, list]: + return out_type([out_merged[i] for i in range(chunk_length)]) + elif out_type is dict: + return out_merged + + +def get_ray_directions( + H: int, + W: int, + focal: Union[float, Tuple[float, float]], + principal: Optional[Tuple[float, float]] = None, + use_pixel_centers: bool = True, +) -> Float[Tensor, "H W 3"]: + """ + Get ray directions for all pixels in camera coordinate. + Reference: https://www.scratchapixel.com/lessons/3d-basic-rendering/ + ray-tracing-generating-camera-rays/standard-coordinate-systems + + Inputs: + H, W, focal, principal, use_pixel_centers: image height, width, focal length, principal point and whether use pixel centers + Outputs: + directions: (H, W, 3), the direction of the rays in camera coordinate + """ + pixel_center = 0.5 if use_pixel_centers else 0 + + if isinstance(focal, float): + fx, fy = focal, focal + cx, cy = W / 2, H / 2 + else: + fx, fy = focal + assert principal is not None + cx, cy = principal + + i, j = torch.meshgrid( + torch.arange(W, dtype=torch.float32) + pixel_center, + torch.arange(H, dtype=torch.float32) + pixel_center, + indexing="xy", + ) + + directions: Float[Tensor, "H W 3"] = torch.stack( + [(i - cx) / fx, -(j - cy) / fy, -torch.ones_like(i)], -1 + ) + + return directions + + +def mask_ray_directions(H: int, W: int, s_H: int, s_W: int) -> Float[Tensor, "s_H s_W"]: + """ + Masking the (H,W) image to (s_H,s_W), for efficient training at higher resolution image. + pixels from (s_H,s_W) are sampled more (1-aspect_ratio) than outside pixels(aspect_ratio). + the masking is deferred to before calling get_rays(). + """ + # indices_all = torch.meshgrid( + # torch.arange(W, dtype=torch.float32) , + # torch.arange(H, dtype=torch.float32) , + # indexing="xy", + # ) + + indices_inner = torch.meshgrid( + torch.linspace(0, 0.75 * W, s_W, dtype=torch.int8), + torch.linspace(0, 0.75 * H, s_H, dtype=torch.int8), + indexing="xy", + ) + offset = [torch.randint(0, W // 8 + 1, (1,)), torch.randint(0, H // 8 + 1, (1,))] + + select_ind = indices_inner[0] + offset[0] + H * (indices_inner[1] + offset[1]) + + ### removing the random sampling approach, we sample in uniform grid + # mask = torch.zeros(H,W, dtype=torch.bool) + # mask[(H-s_H)//2 : H - math.ceil((H-s_H)/2),(W-s_W)//2 : W - math.ceil((W-s_W)/2)] = True + + # in_ind_1d = (indices_all[0]+H*indices_all[1])[mask] + # out_ind_1d = (indices_all[0]+H*indices_all[1])[torch.logical_not(mask)] + # ### tried using 0.5 p ratio of sampling inside vs outside, as smaller area already + # ### leads to more samples inside anyways + + # p = 0.5#(s_H*s_W)/(H*W) + # select_ind = in_ind_1d[ + # torch.multinomial( + # torch.ones_like(in_ind_1d)*(1-p),int((1-p)*(s_H*s_W)),replacement=False)] + # select_ind = torch.concatenate( + # [select_ind, out_ind_1d[torch.multinomial( + # torch.ones_like(out_ind_1d)*(p),int((p)*(s_H*s_W)),replacement=False)] + # ], + # dim=0).to(dtype=torch.int).view(s_H,s_W) + + ### first attempt at sampling, this produces variable number of rays, + ### so 4D tensor directions cant be sampled + # mask = torch.zeros(H,W, device= directions.device) + # p = (s_H*s_W)/(H*W) + # mask += p + # mask[(H-s_H)//2 : H - math.ceil((H-s_H)/2),(W-s_W)//2 : W - math.ceil((W-s_W)/2)] = 1 - p + # ### mask contains prob of individual pixel, drawing using Bernoulli dist + # mask = torch.bernoulli(mask).to(dtype=torch.bool) + ### postponing masking before get_rays is called + # directions = directions[mask] + + return select_ind + + +def get_rays( + directions: Float[Tensor, "... 3"], + c2w: Float[Tensor, "... 4 4"], + keepdim=False, + noise_scale=0.0, + normalize=True, +) -> Tuple[Float[Tensor, "... 3"], Float[Tensor, "... 3"]]: + # Rotate ray directions from camera coordinate to the world coordinate + assert directions.shape[-1] == 3 + + if directions.ndim == 2: # (N_rays, 3) + if c2w.ndim == 2: # (4, 4) + c2w = c2w[None, :, :] + assert c2w.ndim == 3 # (N_rays, 4, 4) or (1, 4, 4) + rays_d = (directions[:, None, :] * c2w[:, :3, :3]).sum(-1) # (N_rays, 3) + rays_o = c2w[:, :3, 3].expand(rays_d.shape) + elif directions.ndim == 3: # (H, W, 3) + assert c2w.ndim in [2, 3] + if c2w.ndim == 2: # (4, 4) + rays_d = (directions[:, :, None, :] * c2w[None, None, :3, :3]).sum( + -1 + ) # (H, W, 3) + rays_o = c2w[None, None, :3, 3].expand(rays_d.shape) + elif c2w.ndim == 3: # (B, 4, 4) + rays_d = (directions[None, :, :, None, :] * c2w[:, None, None, :3, :3]).sum( + -1 + ) # (B, H, W, 3) + rays_o = c2w[:, None, None, :3, 3].expand(rays_d.shape) + elif directions.ndim == 4: # (B, H, W, 3) + assert c2w.ndim == 3 # (B, 4, 4) + rays_d = (directions[:, :, :, None, :] * c2w[:, None, None, :3, :3]).sum( + -1 + ) # (B, H, W, 3) + rays_o = c2w[:, None, None, :3, 3].expand(rays_d.shape) + + # add camera noise to avoid grid-like artifect + # https://github.com/ashawkey/stable-dreamfusion/blob/49c3d4fa01d68a4f027755acf94e1ff6020458cc/nerf/utils.py#L373 + if noise_scale > 0: + rays_o = rays_o + torch.randn(3, device=rays_o.device) * noise_scale + rays_d = rays_d + torch.randn(3, device=rays_d.device) * noise_scale + + if normalize: + rays_d = F.normalize(rays_d, dim=-1) + if not keepdim: + rays_o, rays_d = rays_o.reshape(-1, 3), rays_d.reshape(-1, 3) + + return rays_o, rays_d + + +def get_projection_matrix( + fovy: Float[Tensor, "B"], aspect_wh: float, near: float, far: float +) -> Float[Tensor, "B 4 4"]: + batch_size = fovy.shape[0] + proj_mtx = torch.zeros(batch_size, 4, 4, dtype=torch.float32) + proj_mtx[:, 0, 0] = 1.0 / (torch.tan(fovy / 2.0) * aspect_wh) + proj_mtx[:, 1, 1] = -1.0 / torch.tan( + fovy / 2.0 + ) # add a negative sign here as the y axis is flipped in nvdiffrast output + proj_mtx[:, 2, 2] = -(far + near) / (far - near) + proj_mtx[:, 2, 3] = -2.0 * far * near / (far - near) + proj_mtx[:, 3, 2] = -1.0 + return proj_mtx + + +def get_mvp_matrix( + c2w: Float[Tensor, "B 4 4"], proj_mtx: Float[Tensor, "B 4 4"] +) -> Float[Tensor, "B 4 4"]: + # calculate w2c from c2w: R' = Rt, t' = -Rt * t + # mathematically equivalent to (c2w)^-1 + w2c: Float[Tensor, "B 4 4"] = torch.zeros(c2w.shape[0], 4, 4).to(c2w) + w2c[:, :3, :3] = c2w[:, :3, :3].permute(0, 2, 1) + w2c[:, :3, 3:] = -c2w[:, :3, :3].permute(0, 2, 1) @ c2w[:, :3, 3:] + w2c[:, 3, 3] = 1.0 + # calculate mvp matrix by proj_mtx @ w2c (mv_mtx) + mvp_mtx = proj_mtx @ w2c + return mvp_mtx + + +def get_full_projection_matrix( + c2w: Float[Tensor, "B 4 4"], proj_mtx: Float[Tensor, "B 4 4"] +) -> Float[Tensor, "B 4 4"]: + return (c2w.unsqueeze(0).bmm(proj_mtx.unsqueeze(0))).squeeze(0) + + +# gaussian splatting functions +def convert_pose(C2W): + flip_yz = torch.eye(4, device=C2W.device) + flip_yz[1, 1] = -1 + flip_yz[2, 2] = -1 + C2W = torch.matmul(C2W, flip_yz) + return C2W + + +def get_projection_matrix_gaussian(znear, zfar, fovX, fovY, device="cuda"): + tanHalfFovY = math.tan((fovY / 2)) + tanHalfFovX = math.tan((fovX / 2)) + + top = tanHalfFovY * znear + bottom = -top + right = tanHalfFovX * znear + left = -right + + P = torch.zeros(4, 4, device=device) + + z_sign = 1.0 + + P[0, 0] = 2.0 * znear / (right - left) + P[1, 1] = 2.0 * znear / (top - bottom) + P[0, 2] = (right + left) / (right - left) + P[1, 2] = (top + bottom) / (top - bottom) + P[3, 2] = z_sign + P[2, 2] = z_sign * zfar / (zfar - znear) + P[2, 3] = -(zfar * znear) / (zfar - znear) + return P + + +def get_fov_gaussian(P): + tanHalfFovX = 1 / P[0, 0] + tanHalfFovY = 1 / P[1, 1] + fovY = math.atan(tanHalfFovY) * 2 + fovX = math.atan(tanHalfFovX) * 2 + return fovX, fovY + + +def get_cam_info_gaussian(c2w, fovx, fovy, znear, zfar): + c2w = convert_pose(c2w) + world_view_transform = torch.inverse(c2w) + + world_view_transform = world_view_transform.transpose(0, 1).cuda().float() + projection_matrix = ( + get_projection_matrix_gaussian(znear=znear, zfar=zfar, fovX=fovx, fovY=fovy) + .transpose(0, 1) + .cuda() + ) + full_proj_transform = ( + world_view_transform.unsqueeze(0).bmm(projection_matrix.unsqueeze(0)) + ).squeeze(0) + camera_center = world_view_transform.inverse()[3, :3] + + return world_view_transform, full_proj_transform, camera_center + + +def binary_cross_entropy(input, target): + """ + F.binary_cross_entropy is not numerically stable in mixed-precision training. + """ + return -(target * torch.log(input) + (1 - target) * torch.log(1 - input)).mean() + + +def tet_sdf_diff( + vert_sdf: Float[Tensor, "Nv 1"], tet_edges: Integer[Tensor, "Ne 2"] +) -> Float[Tensor, ""]: + sdf_f1x6x2 = vert_sdf[:, 0][tet_edges.reshape(-1)].reshape(-1, 2) + mask = torch.sign(sdf_f1x6x2[..., 0]) != torch.sign(sdf_f1x6x2[..., 1]) + sdf_f1x6x2 = sdf_f1x6x2[mask] + sdf_diff = F.binary_cross_entropy_with_logits( + sdf_f1x6x2[..., 0], (sdf_f1x6x2[..., 1] > 0).float() + ) + F.binary_cross_entropy_with_logits( + sdf_f1x6x2[..., 1], (sdf_f1x6x2[..., 0] > 0).float() + ) + return sdf_diff + + +# Implementation from Latent-NeRF +# https://github.com/eladrich/latent-nerf/blob/f49ecefcd48972e69a28e3116fe95edf0fac4dc8/src/latent_nerf/models/mesh_utils.py +class MeshOBJ: + dx = torch.zeros(3).float() + dx[0] = 1 + dy, dz = dx[[1, 0, 2]], dx[[2, 1, 0]] + dx, dy, dz = dx[None, :], dy[None, :], dz[None, :] + + def __init__(self, v: np.ndarray, f: np.ndarray): + self.v = v + self.f = f + self.dx, self.dy, self.dz = MeshOBJ.dx, MeshOBJ.dy, MeshOBJ.dz + self.v_tensor = torch.from_numpy(self.v) + + vf = self.v[self.f, :] + self.f_center = vf.mean(axis=1) + self.f_center_tensor = torch.from_numpy(self.f_center).float() + + e1 = vf[:, 1, :] - vf[:, 0, :] + e2 = vf[:, 2, :] - vf[:, 0, :] + self.face_normals = np.cross(e1, e2) + self.face_normals = ( + self.face_normals / np.linalg.norm(self.face_normals, axis=-1)[:, None] + ) + self.face_normals_tensor = torch.from_numpy(self.face_normals) + + def normalize_mesh(self, target_scale=0.5): + verts = self.v + + # Compute center of bounding box + # center = torch.mean(torch.column_stack([torch.max(verts, dim=0)[0], torch.min(verts, dim=0)[0]])) + center = verts.mean(axis=0) + verts = verts - center + scale = np.max(np.linalg.norm(verts, axis=1)) + verts = (verts / scale) * target_scale + + return MeshOBJ(verts, self.f) + + def winding_number(self, query: torch.Tensor): + device = query.device + shp = query.shape + query_np = query.detach().cpu().reshape(-1, 3).numpy() + target_alphas = fast_winding_number_for_meshes( + self.v.astype(np.float32), self.f, query_np + ) + return torch.from_numpy(target_alphas).reshape(shp[:-1]).to(device) + + def gaussian_weighted_distance(self, query: torch.Tensor, sigma): + device = query.device + shp = query.shape + query_np = query.detach().cpu().reshape(-1, 3).numpy() + distances, _, _ = point_mesh_squared_distance( + query_np, self.v.astype(np.float32), self.f + ) + distances = torch.from_numpy(distances).reshape(shp[:-1]).to(device) + weight = torch.exp(-(distances / (2 * sigma**2))) + return weight + + +def ce_pq_loss(p, q, weight=None): + def clamp(v, T=0.0001): + return v.clamp(T, 1 - T) + + p = p.view(q.shape) + ce = -1 * (p * torch.log(clamp(q)) + (1 - p) * torch.log(clamp(1 - q))) + if weight is not None: + ce *= weight + return ce.sum() + + +class ShapeLoss(nn.Module): + def __init__(self, guide_shape): + super().__init__() + self.mesh_scale = 0.7 + self.proximal_surface = 0.3 + self.delta = 0.2 + self.shape_path = guide_shape + v, _, _, f, _, _ = read_obj(self.shape_path, float) + mesh = MeshOBJ(v, f) + matrix_rot = np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]]) @ np.array( + [[0, 0, 1], [0, 1, 0], [-1, 0, 0]] + ) + self.sketchshape = mesh.normalize_mesh(self.mesh_scale) + self.sketchshape = MeshOBJ( + np.ascontiguousarray( + (matrix_rot @ self.sketchshape.v.transpose(1, 0)).transpose(1, 0) + ), + f, + ) + + def forward(self, xyzs, sigmas): + mesh_occ = self.sketchshape.winding_number(xyzs) + if self.proximal_surface > 0: + weight = 1 - self.sketchshape.gaussian_weighted_distance( + xyzs, self.proximal_surface + ) + else: + weight = None + indicator = (mesh_occ > 0.5).float() + nerf_occ = 1 - torch.exp(-self.delta * sigmas) + nerf_occ = nerf_occ.clamp(min=0, max=1.1) + loss = ce_pq_loss( + nerf_occ, indicator, weight=weight + ) # order is important for CE loss + second argument may not be optimized + return loss + + +def shifted_expotional_decay(a, b, c, r): + return a * torch.exp(-b * r) + c + + +def shifted_cosine_decay(a, b, c, r): + return a * torch.cos(b * r + c) + a + + +def perpendicular_component(x: Float[Tensor, "B C H W"], y: Float[Tensor, "B C H W"]): + # get the component of x that is perpendicular to y + eps = torch.ones_like(x[:, 0, 0, 0]) * 1e-6 + return ( + x + - ( + torch.mul(x, y).sum(dim=[1, 2, 3]) + / torch.maximum(torch.mul(y, y).sum(dim=[1, 2, 3]), eps) + ).view(-1, 1, 1, 1) + * y + ) + + +def validate_empty_rays(ray_indices, t_start, t_end): + if ray_indices.nelement() == 0: + threestudio.warn("Empty rays_indices!") + ray_indices = torch.LongTensor([0]).to(ray_indices) + t_start = torch.Tensor([0]).to(ray_indices) + t_end = torch.Tensor([0]).to(ray_indices) + return ray_indices, t_start, t_end diff --git a/threestudio/utils/perceptual/__init__.py b/threestudio/utils/perceptual/__init__.py new file mode 100644 index 0000000..a4d2c7a --- /dev/null +++ b/threestudio/utils/perceptual/__init__.py @@ -0,0 +1 @@ +from .perceptual import PerceptualLoss diff --git a/threestudio/utils/perceptual/perceptual.py b/threestudio/utils/perceptual/perceptual.py new file mode 100644 index 0000000..403d9a9 --- /dev/null +++ b/threestudio/utils/perceptual/perceptual.py @@ -0,0 +1,170 @@ +"""Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models""" + +from collections import namedtuple +from dataclasses import dataclass, field + +import torch +import torch.nn as nn +from torchvision import models + +import threestudio +from threestudio.utils.base import BaseObject +from threestudio.utils.perceptual.utils import get_ckpt_path +from threestudio.utils.typing import * + + +@threestudio.register("perceptual-loss") +class PerceptualLossObject(BaseObject): + @dataclass + class Config(BaseObject.Config): + use_dropout: bool = True + + cfg: Config + + def configure(self) -> None: + self.perceptual_loss = PerceptualLoss(self.cfg.use_dropout).to(self.device) + + def __call__( + self, + x: Float[Tensor, "B 3 256 256"], + y: Float[Tensor, "B 3 256 256"], + ): + return self.perceptual_loss(x, y) + + +class PerceptualLoss(nn.Module): + # Learned perceptual metric + def __init__(self, use_dropout=True): + super().__init__() + self.scaling_layer = ScalingLayer() + self.chns = [64, 128, 256, 512, 512] # vg16 features + self.net = vgg16(pretrained=True, requires_grad=False) + self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout) + self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout) + self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout) + self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout) + self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout) + self.load_from_pretrained() + for param in self.parameters(): + param.requires_grad = False + + def load_from_pretrained(self, name="vgg_lpips"): + ckpt = get_ckpt_path(name, "threestudio/utils/lpips") + self.load_state_dict( + torch.load(ckpt, map_location=torch.device("cpu")), strict=False + ) + print("loaded pretrained LPIPS loss from {}".format(ckpt)) + + @classmethod + def from_pretrained(cls, name="vgg_lpips"): + if name != "vgg_lpips": + raise NotImplementedError + model = cls() + ckpt = get_ckpt_path(name) + model.load_state_dict( + torch.load(ckpt, map_location=torch.device("cpu")), strict=False + ) + return model + + def forward(self, input, target): + in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target)) + outs0, outs1 = self.net(in0_input), self.net(in1_input) + feats0, feats1, diffs = {}, {}, {} + lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4] + for kk in range(len(self.chns)): + feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor( + outs1[kk] + ) + diffs[kk] = (feats0[kk] - feats1[kk]) ** 2 + + res = [ + spatial_average(lins[kk].model(diffs[kk]), keepdim=True) + for kk in range(len(self.chns)) + ] + val = res[0] + for l in range(1, len(self.chns)): + val += res[l] + return val + + +class ScalingLayer(nn.Module): + def __init__(self): + super(ScalingLayer, self).__init__() + self.register_buffer( + "shift", torch.Tensor([-0.030, -0.088, -0.188])[None, :, None, None] + ) + self.register_buffer( + "scale", torch.Tensor([0.458, 0.448, 0.450])[None, :, None, None] + ) + + def forward(self, inp): + return (inp - self.shift) / self.scale + + +class NetLinLayer(nn.Module): + """A single linear layer which does a 1x1 conv""" + + def __init__(self, chn_in, chn_out=1, use_dropout=False): + super(NetLinLayer, self).__init__() + layers = ( + [ + nn.Dropout(), + ] + if (use_dropout) + else [] + ) + layers += [ + nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), + ] + self.model = nn.Sequential(*layers) + + +class vgg16(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(vgg16, self).__init__() + vgg_pretrained_features = models.vgg16(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.N_slices = 5 + for x in range(4): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(4, 9): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(9, 16): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(16, 23): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + for x in range(23, 30): + self.slice5.add_module(str(x), vgg_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1_2 = h + h = self.slice2(h) + h_relu2_2 = h + h = self.slice3(h) + h_relu3_3 = h + h = self.slice4(h) + h_relu4_3 = h + h = self.slice5(h) + h_relu5_3 = h + vgg_outputs = namedtuple( + "VggOutputs", ["relu1_2", "relu2_2", "relu3_3", "relu4_3", "relu5_3"] + ) + out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3) + return out + + +def normalize_tensor(x, eps=1e-10): + norm_factor = torch.sqrt(torch.sum(x**2, dim=1, keepdim=True)) + return x / (norm_factor + eps) + + +def spatial_average(x, keepdim=True): + return x.mean([2, 3], keepdim=keepdim) diff --git a/threestudio/utils/perceptual/utils.py b/threestudio/utils/perceptual/utils.py new file mode 100644 index 0000000..5da48b3 --- /dev/null +++ b/threestudio/utils/perceptual/utils.py @@ -0,0 +1,154 @@ +import hashlib +import os + +import requests +from tqdm import tqdm + +URL_MAP = {"vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1"} + +CKPT_MAP = {"vgg_lpips": "vgg.pth"} + +MD5_MAP = {"vgg_lpips": "d507d7349b931f0638a25a48a722f98a"} + + +def download(url, local_path, chunk_size=1024): + os.makedirs(os.path.split(local_path)[0], exist_ok=True) + with requests.get(url, stream=True) as r: + total_size = int(r.headers.get("content-length", 0)) + with tqdm(total=total_size, unit="B", unit_scale=True) as pbar: + with open(local_path, "wb") as f: + for data in r.iter_content(chunk_size=chunk_size): + if data: + f.write(data) + pbar.update(chunk_size) + + +def md5_hash(path): + with open(path, "rb") as f: + content = f.read() + return hashlib.md5(content).hexdigest() + + +def get_ckpt_path(name, root, check=False): + assert name in URL_MAP + path = os.path.join(root, CKPT_MAP[name]) + if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]): + print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path)) + download(URL_MAP[name], path) + md5 = md5_hash(path) + assert md5 == MD5_MAP[name], md5 + return path + + +class KeyNotFoundError(Exception): + def __init__(self, cause, keys=None, visited=None): + self.cause = cause + self.keys = keys + self.visited = visited + messages = list() + if keys is not None: + messages.append("Key not found: {}".format(keys)) + if visited is not None: + messages.append("Visited: {}".format(visited)) + messages.append("Cause:\n{}".format(cause)) + message = "\n".join(messages) + super().__init__(message) + + +def retrieve( + list_or_dict, key, splitval="/", default=None, expand=True, pass_success=False +): + """Given a nested list or dict return the desired value at key expanding + callable nodes if necessary and :attr:`expand` is ``True``. The expansion + is done in-place. + + Parameters + ---------- + list_or_dict : list or dict + Possibly nested list or dictionary. + key : str + key/to/value, path like string describing all keys necessary to + consider to get to the desired value. List indices can also be + passed here. + splitval : str + String that defines the delimiter between keys of the + different depth levels in `key`. + default : obj + Value returned if :attr:`key` is not found. + expand : bool + Whether to expand callable nodes on the path or not. + + Returns + ------- + The desired value or if :attr:`default` is not ``None`` and the + :attr:`key` is not found returns ``default``. + + Raises + ------ + Exception if ``key`` not in ``list_or_dict`` and :attr:`default` is + ``None``. + """ + + keys = key.split(splitval) + + success = True + try: + visited = [] + parent = None + last_key = None + for key in keys: + if callable(list_or_dict): + if not expand: + raise KeyNotFoundError( + ValueError( + "Trying to get past callable node with expand=False." + ), + keys=keys, + visited=visited, + ) + list_or_dict = list_or_dict() + parent[last_key] = list_or_dict + + last_key = key + parent = list_or_dict + + try: + if isinstance(list_or_dict, dict): + list_or_dict = list_or_dict[key] + else: + list_or_dict = list_or_dict[int(key)] + except (KeyError, IndexError, ValueError) as e: + raise KeyNotFoundError(e, keys=keys, visited=visited) + + visited += [key] + # final expansion of retrieved value + if expand and callable(list_or_dict): + list_or_dict = list_or_dict() + parent[last_key] = list_or_dict + except KeyNotFoundError as e: + if default is None: + raise e + else: + list_or_dict = default + success = False + + if not pass_success: + return list_or_dict + else: + return list_or_dict, success + + +if __name__ == "__main__": + config = { + "keya": "a", + "keyb": "b", + "keyc": { + "cc1": 1, + "cc2": 2, + }, + } + from omegaconf import OmegaConf + + config = OmegaConf.create(config) + print(config) + retrieve(config, "keya") diff --git a/threestudio/utils/rasterize.py b/threestudio/utils/rasterize.py new file mode 100644 index 0000000..a174bc1 --- /dev/null +++ b/threestudio/utils/rasterize.py @@ -0,0 +1,78 @@ +import nvdiffrast.torch as dr +import torch + +from threestudio.utils.typing import * + + +class NVDiffRasterizerContext: + def __init__(self, context_type: str, device: torch.device) -> None: + self.device = device + self.ctx = self.initialize_context(context_type, device) + + def initialize_context( + self, context_type: str, device: torch.device + ) -> Union[dr.RasterizeGLContext, dr.RasterizeCudaContext]: + if context_type == "gl": + return dr.RasterizeGLContext(device=device) + elif context_type == "cuda": + return dr.RasterizeCudaContext(device=device) + else: + raise ValueError(f"Unknown rasterizer context type: {context_type}") + + def vertex_transform( + self, verts: Float[Tensor, "Nv 3"], mvp_mtx: Float[Tensor, "B 4 4"] + ) -> Float[Tensor, "B Nv 4"]: + verts_homo = torch.cat( + [verts, torch.ones([verts.shape[0], 1]).to(verts)], dim=-1 + ) + return torch.matmul(verts_homo, mvp_mtx.permute(0, 2, 1)) + + def rasterize( + self, + pos: Float[Tensor, "B Nv 4"], + tri: Integer[Tensor, "Nf 3"], + resolution: Union[int, Tuple[int, int]], + ): + # rasterize in instance mode (single topology) + return dr.rasterize(self.ctx, pos.float(), tri.int(), resolution, grad_db=True) + + def rasterize_one( + self, + pos: Float[Tensor, "Nv 4"], + tri: Integer[Tensor, "Nf 3"], + resolution: Union[int, Tuple[int, int]], + ): + # rasterize one single mesh under a single viewpoint + rast, rast_db = self.rasterize(pos[None, ...], tri, resolution) + return rast[0], rast_db[0] + + def antialias( + self, + color: Float[Tensor, "B H W C"], + rast: Float[Tensor, "B H W 4"], + pos: Float[Tensor, "B Nv 4"], + tri: Integer[Tensor, "Nf 3"], + ) -> Float[Tensor, "B H W C"]: + return dr.antialias(color.float(), rast, pos.float(), tri.int()) + + def interpolate( + self, + attr: Float[Tensor, "B Nv C"], + rast: Float[Tensor, "B H W 4"], + tri: Integer[Tensor, "Nf 3"], + rast_db=None, + diff_attrs=None, + ) -> Float[Tensor, "B H W C"]: + return dr.interpolate( + attr.float(), rast, tri.int(), rast_db=rast_db, diff_attrs=diff_attrs + ) + + def interpolate_one( + self, + attr: Float[Tensor, "Nv C"], + rast: Float[Tensor, "B H W 4"], + tri: Integer[Tensor, "Nf 3"], + rast_db=None, + diff_attrs=None, + ) -> Float[Tensor, "B H W C"]: + return self.interpolate(attr[None, ...], rast, tri, rast_db, diff_attrs) diff --git a/threestudio/utils/saving.py b/threestudio/utils/saving.py new file mode 100644 index 0000000..a9040fa --- /dev/null +++ b/threestudio/utils/saving.py @@ -0,0 +1,652 @@ +import json +import os +import re +import shutil + +import cv2 +import imageio +import matplotlib.pyplot as plt +import numpy as np +import torch +import trimesh +import wandb +from matplotlib import cm +from matplotlib.colors import LinearSegmentedColormap +from PIL import Image, ImageDraw +from pytorch_lightning.loggers import WandbLogger + +from threestudio.models.mesh import Mesh +from threestudio.utils.typing import * + + +class SaverMixin: + _save_dir: Optional[str] = None + _wandb_logger: Optional[WandbLogger] = None + + def set_save_dir(self, save_dir: str): + self._save_dir = save_dir + + def get_save_dir(self): + if self._save_dir is None: + raise ValueError("Save dir is not set") + return self._save_dir + + def convert_data(self, data): + if data is None: + return None + elif isinstance(data, np.ndarray): + return data + elif isinstance(data, torch.Tensor): + return data.detach().cpu().numpy() + elif isinstance(data, list): + return [self.convert_data(d) for d in data] + elif isinstance(data, dict): + return {k: self.convert_data(v) for k, v in data.items()} + else: + raise TypeError( + "Data must be in type numpy.ndarray, torch.Tensor, list or dict, getting", + type(data), + ) + + def get_save_path(self, filename): + save_path = os.path.join(self.get_save_dir(), filename) + os.makedirs(os.path.dirname(save_path), exist_ok=True) + return save_path + + def create_loggers(self, cfg_loggers: DictConfig) -> None: + if "wandb" in cfg_loggers.keys() and cfg_loggers.wandb.enable: + self._wandb_logger = WandbLogger( + project=cfg_loggers.wandb.project, name=cfg_loggers.wandb.name + ) + + def get_loggers(self) -> List: + if self._wandb_logger: + return [self._wandb_logger] + else: + return [] + + DEFAULT_RGB_KWARGS = {"data_format": "HWC", "data_range": (0, 1)} + DEFAULT_UV_KWARGS = { + "data_format": "HWC", + "data_range": (0, 1), + "cmap": "checkerboard", + } + DEFAULT_GRAYSCALE_KWARGS = {"data_range": None, "cmap": "jet"} + DEFAULT_GRID_KWARGS = {"align": "max"} + + def get_rgb_image_(self, img, data_format, data_range, rgba=False): + img = self.convert_data(img) + assert data_format in ["CHW", "HWC"] + if data_format == "CHW": + img = img.transpose(1, 2, 0) + if img.dtype != np.uint8: + img = img.clip(min=data_range[0], max=data_range[1]) + img = ( + (img - data_range[0]) / (data_range[1] - data_range[0]) * 255.0 + ).astype(np.uint8) + nc = 4 if rgba else 3 + imgs = [img[..., start : start + nc] for start in range(0, img.shape[-1], nc)] + imgs = [ + img_ + if img_.shape[-1] == nc + else np.concatenate( + [ + img_, + np.zeros( + (img_.shape[0], img_.shape[1], nc - img_.shape[2]), + dtype=img_.dtype, + ), + ], + axis=-1, + ) + for img_ in imgs + ] + img = np.concatenate(imgs, axis=1) + if rgba: + img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGRA) + else: + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + return img + + def _save_rgb_image( + self, + filename, + img, + data_format, + data_range, + name: Optional[str] = None, + step: Optional[int] = None, + ): + img = self.get_rgb_image_(img, data_format, data_range) + cv2.imwrite(filename, img) + if name and self._wandb_logger: + wandb.log( + { + name: wandb.Image(self.get_save_path(filename)), + "trainer/global_step": step, + } + ) + + def save_rgb_image( + self, + filename, + img, + data_format=DEFAULT_RGB_KWARGS["data_format"], + data_range=DEFAULT_RGB_KWARGS["data_range"], + name: Optional[str] = None, + step: Optional[int] = None, + ) -> str: + save_path = self.get_save_path(filename) + self._save_rgb_image(save_path, img, data_format, data_range, name, step) + return save_path + + def get_uv_image_(self, img, data_format, data_range, cmap): + img = self.convert_data(img) + assert data_format in ["CHW", "HWC"] + if data_format == "CHW": + img = img.transpose(1, 2, 0) + img = img.clip(min=data_range[0], max=data_range[1]) + img = (img - data_range[0]) / (data_range[1] - data_range[0]) + assert cmap in ["checkerboard", "color"] + if cmap == "checkerboard": + n_grid = 64 + mask = (img * n_grid).astype(int) + mask = (mask[..., 0] + mask[..., 1]) % 2 == 0 + img = np.ones((img.shape[0], img.shape[1], 3), dtype=np.uint8) * 255 + img[mask] = np.array([255, 0, 255], dtype=np.uint8) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + elif cmap == "color": + img_ = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8) + img_[..., 0] = (img[..., 0] * 255).astype(np.uint8) + img_[..., 1] = (img[..., 1] * 255).astype(np.uint8) + img_ = cv2.cvtColor(img_, cv2.COLOR_RGB2BGR) + img = img_ + return img + + def save_uv_image( + self, + filename, + img, + data_format=DEFAULT_UV_KWARGS["data_format"], + data_range=DEFAULT_UV_KWARGS["data_range"], + cmap=DEFAULT_UV_KWARGS["cmap"], + ) -> str: + save_path = self.get_save_path(filename) + img = self.get_uv_image_(img, data_format, data_range, cmap) + cv2.imwrite(save_path, img) + return save_path + + def get_grayscale_image_(self, img, data_range, cmap): + img = self.convert_data(img) + img = np.nan_to_num(img) + if data_range is None: + img = (img - img.min()) / (img.max() - img.min()) + else: + img = img.clip(data_range[0], data_range[1]) + img = (img - data_range[0]) / (data_range[1] - data_range[0]) + assert cmap in [None, "jet", "magma", "spectral"] + if cmap == None: + img = (img * 255.0).astype(np.uint8) + img = np.repeat(img[..., None], 3, axis=2) + elif cmap == "jet": + img = (img * 255.0).astype(np.uint8) + img = cv2.applyColorMap(img, cv2.COLORMAP_JET) + elif cmap == "magma": + img = 1.0 - img + base = cm.get_cmap("magma") + num_bins = 256 + colormap = LinearSegmentedColormap.from_list( + f"{base.name}{num_bins}", base(np.linspace(0, 1, num_bins)), num_bins + )(np.linspace(0, 1, num_bins))[:, :3] + a = np.floor(img * 255.0) + b = (a + 1).clip(max=255.0) + f = img * 255.0 - a + a = a.astype(np.uint16).clip(0, 255) + b = b.astype(np.uint16).clip(0, 255) + img = colormap[a] + (colormap[b] - colormap[a]) * f[..., None] + img = (img * 255.0).astype(np.uint8) + elif cmap == "spectral": + colormap = plt.get_cmap("Spectral") + + def blend_rgba(image): + image = image[..., :3] * image[..., -1:] + ( + 1.0 - image[..., -1:] + ) # blend A to RGB + return image + + img = colormap(img) + img = blend_rgba(img) + img = (img * 255).astype(np.uint8) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + return img + + def _save_grayscale_image( + self, + filename, + img, + data_range, + cmap, + name: Optional[str] = None, + step: Optional[int] = None, + ): + img = self.get_grayscale_image_(img, data_range, cmap) + cv2.imwrite(filename, img) + if name and self._wandb_logger: + wandb.log( + { + name: wandb.Image(self.get_save_path(filename)), + "trainer/global_step": step, + } + ) + + def save_grayscale_image( + self, + filename, + img, + data_range=DEFAULT_GRAYSCALE_KWARGS["data_range"], + cmap=DEFAULT_GRAYSCALE_KWARGS["cmap"], + name: Optional[str] = None, + step: Optional[int] = None, + ) -> str: + save_path = self.get_save_path(filename) + self._save_grayscale_image(save_path, img, data_range, cmap, name, step) + return save_path + + def get_image_grid_(self, imgs, align): + if isinstance(imgs[0], list): + return np.concatenate( + [self.get_image_grid_(row, align) for row in imgs], axis=0 + ) + cols = [] + for col in imgs: + assert col["type"] in ["rgb", "uv", "grayscale"] + if col["type"] == "rgb": + rgb_kwargs = self.DEFAULT_RGB_KWARGS.copy() + rgb_kwargs.update(col["kwargs"]) + cols.append(self.get_rgb_image_(col["img"], **rgb_kwargs)) + elif col["type"] == "uv": + uv_kwargs = self.DEFAULT_UV_KWARGS.copy() + uv_kwargs.update(col["kwargs"]) + cols.append(self.get_uv_image_(col["img"], **uv_kwargs)) + elif col["type"] == "grayscale": + grayscale_kwargs = self.DEFAULT_GRAYSCALE_KWARGS.copy() + grayscale_kwargs.update(col["kwargs"]) + cols.append(self.get_grayscale_image_(col["img"], **grayscale_kwargs)) + + if align == "max": + h = max([col.shape[0] for col in cols]) + w = max([col.shape[1] for col in cols]) + elif align == "min": + h = min([col.shape[0] for col in cols]) + w = min([col.shape[1] for col in cols]) + elif isinstance(align, int): + h = align + w = align + elif ( + isinstance(align, tuple) + and isinstance(align[0], int) + and isinstance(align[1], int) + ): + h, w = align + else: + raise ValueError( + f"Unsupported image grid align: {align}, should be min, max, int or (int, int)" + ) + + for i in range(len(cols)): + if cols[i].shape[0] != h or cols[i].shape[1] != w: + cols[i] = cv2.resize(cols[i], (w, h), interpolation=cv2.INTER_LINEAR) + return np.concatenate(cols, axis=1) + + def save_image_grid( + self, + filename, + imgs, + align=DEFAULT_GRID_KWARGS["align"], + name: Optional[str] = None, + step: Optional[int] = None, + texts: Optional[List[float]] = None, + ): + save_path = self.get_save_path(filename) + img = self.get_image_grid_(imgs, align=align) + + if texts is not None: + img = Image.fromarray(img) + draw = ImageDraw.Draw(img) + black, white = (0, 0, 0), (255, 255, 255) + for i, text in enumerate(texts): + draw.text((2, (img.size[1] // len(texts)) * i + 1), f"{text}", white) + draw.text((0, (img.size[1] // len(texts)) * i + 1), f"{text}", white) + draw.text((2, (img.size[1] // len(texts)) * i - 1), f"{text}", white) + draw.text((0, (img.size[1] // len(texts)) * i - 1), f"{text}", white) + draw.text((1, (img.size[1] // len(texts)) * i), f"{text}", black) + img = np.asarray(img) + + cv2.imwrite(save_path, img) + if name and self._wandb_logger: + wandb.log({name: wandb.Image(save_path), "trainer/global_step": step}) + return save_path + + def save_image(self, filename, img) -> str: + save_path = self.get_save_path(filename) + img = self.convert_data(img) + assert img.dtype == np.uint8 or img.dtype == np.uint16 + if img.ndim == 3 and img.shape[-1] == 3: + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + elif img.ndim == 3 and img.shape[-1] == 4: + img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGRA) + cv2.imwrite(save_path, img) + return save_path + + def save_cubemap(self, filename, img, data_range=(0, 1), rgba=False) -> str: + save_path = self.get_save_path(filename) + img = self.convert_data(img) + assert img.ndim == 4 and img.shape[0] == 6 and img.shape[1] == img.shape[2] + + imgs_full = [] + for start in range(0, img.shape[-1], 3): + img_ = img[..., start : start + 3] + img_ = np.stack( + [ + self.get_rgb_image_(img_[i], "HWC", data_range, rgba=rgba) + for i in range(img_.shape[0]) + ], + axis=0, + ) + size = img_.shape[1] + placeholder = np.zeros((size, size, 3), dtype=np.float32) + img_full = np.concatenate( + [ + np.concatenate( + [placeholder, img_[2], placeholder, placeholder], axis=1 + ), + np.concatenate([img_[1], img_[4], img_[0], img_[5]], axis=1), + np.concatenate( + [placeholder, img_[3], placeholder, placeholder], axis=1 + ), + ], + axis=0, + ) + imgs_full.append(img_full) + + imgs_full = np.concatenate(imgs_full, axis=1) + cv2.imwrite(save_path, imgs_full) + return save_path + + def save_data(self, filename, data) -> str: + data = self.convert_data(data) + if isinstance(data, dict): + if not filename.endswith(".npz"): + filename += ".npz" + save_path = self.get_save_path(filename) + np.savez(save_path, **data) + else: + if not filename.endswith(".npy"): + filename += ".npy" + save_path = self.get_save_path(filename) + np.save(save_path, data) + return save_path + + def save_state_dict(self, filename, data) -> str: + save_path = self.get_save_path(filename) + torch.save(data, save_path) + return save_path + + def save_img_sequence( + self, + filename, + img_dir, + matcher, + save_format="mp4", + fps=30, + name: Optional[str] = None, + step: Optional[int] = None, + ) -> str: + assert save_format in ["gif", "mp4"] + if not filename.endswith(save_format): + filename += f".{save_format}" + save_path = self.get_save_path(filename) + matcher = re.compile(matcher) + img_dir = os.path.join(self.get_save_dir(), img_dir) + imgs = [] + for f in os.listdir(img_dir): + if matcher.search(f): + imgs.append(f) + imgs = sorted(imgs, key=lambda f: int(matcher.search(f).groups()[0])) + imgs = [cv2.imread(os.path.join(img_dir, f)) for f in imgs] + + if save_format == "gif": + imgs = [cv2.cvtColor(i, cv2.COLOR_BGR2RGB) for i in imgs] + imageio.mimsave(save_path, imgs, fps=fps, palettesize=256) + elif save_format == "mp4": + imgs = [cv2.cvtColor(i, cv2.COLOR_BGR2RGB) for i in imgs] + imageio.mimsave(save_path, imgs, fps=fps) + if name and self._wandb_logger: + wandb.log( + { + name: wandb.Video(save_path, format="mp4"), + "trainer/global_step": step, + } + ) + return save_path + + def save_mesh(self, filename, v_pos, t_pos_idx, v_tex=None, t_tex_idx=None) -> str: + save_path = self.get_save_path(filename) + v_pos = self.convert_data(v_pos) + t_pos_idx = self.convert_data(t_pos_idx) + mesh = trimesh.Trimesh(vertices=v_pos, faces=t_pos_idx) + mesh.export(save_path) + return save_path + + def save_obj( + self, + filename: str, + mesh: Mesh, + save_mat: bool = False, + save_normal: bool = False, + save_uv: bool = False, + save_vertex_color: bool = False, + map_Kd: Optional[Float[Tensor, "H W 3"]] = None, + map_Ks: Optional[Float[Tensor, "H W 3"]] = None, + map_Bump: Optional[Float[Tensor, "H W 3"]] = None, + map_Pm: Optional[Float[Tensor, "H W 1"]] = None, + map_Pr: Optional[Float[Tensor, "H W 1"]] = None, + map_format: str = "jpg", + ) -> List[str]: + save_paths: List[str] = [] + if not filename.endswith(".obj"): + filename += ".obj" + v_pos, t_pos_idx = self.convert_data(mesh.v_pos), self.convert_data( + mesh.t_pos_idx + ) + v_nrm, v_tex, t_tex_idx, v_rgb = None, None, None, None + if save_normal: + v_nrm = self.convert_data(mesh.v_nrm) + if save_uv: + v_tex, t_tex_idx = self.convert_data(mesh.v_tex), self.convert_data( + mesh.t_tex_idx + ) + if save_vertex_color: + v_rgb = self.convert_data(mesh.v_rgb) + matname, mtllib = None, None + if save_mat: + matname = "default" + mtl_filename = filename.replace(".obj", ".mtl") + mtllib = os.path.basename(mtl_filename) + mtl_save_paths = self._save_mtl( + mtl_filename, + matname, + map_Kd=self.convert_data(map_Kd), + map_Ks=self.convert_data(map_Ks), + map_Bump=self.convert_data(map_Bump), + map_Pm=self.convert_data(map_Pm), + map_Pr=self.convert_data(map_Pr), + map_format=map_format, + ) + save_paths += mtl_save_paths + obj_save_path = self._save_obj( + filename, + v_pos, + t_pos_idx, + v_nrm=v_nrm, + v_tex=v_tex, + t_tex_idx=t_tex_idx, + v_rgb=v_rgb, + matname=matname, + mtllib=mtllib, + ) + save_paths.append(obj_save_path) + return save_paths + + def _save_obj( + self, + filename, + v_pos, + t_pos_idx, + v_nrm=None, + v_tex=None, + t_tex_idx=None, + v_rgb=None, + matname=None, + mtllib=None, + ) -> str: + obj_str = "" + if matname is not None: + obj_str += f"mtllib {mtllib}\n" + obj_str += f"g object\n" + obj_str += f"usemtl {matname}\n" + for i in range(len(v_pos)): + obj_str += f"v {v_pos[i][0]} {v_pos[i][1]} {v_pos[i][2]}" + if v_rgb is not None: + obj_str += f" {v_rgb[i][0]} {v_rgb[i][1]} {v_rgb[i][2]}" + obj_str += "\n" + if v_nrm is not None: + for v in v_nrm: + obj_str += f"vn {v[0]} {v[1]} {v[2]}\n" + if v_tex is not None: + for v in v_tex: + obj_str += f"vt {v[0]} {1.0 - v[1]}\n" + + for i in range(len(t_pos_idx)): + obj_str += "f" + for j in range(3): + obj_str += f" {t_pos_idx[i][j] + 1}/" + if v_tex is not None: + obj_str += f"{t_tex_idx[i][j] + 1}" + obj_str += "/" + if v_nrm is not None: + obj_str += f"{t_pos_idx[i][j] + 1}" + obj_str += "\n" + + save_path = self.get_save_path(filename) + with open(save_path, "w") as f: + f.write(obj_str) + return save_path + + def _save_mtl( + self, + filename, + matname, + Ka=(0.0, 0.0, 0.0), + Kd=(1.0, 1.0, 1.0), + Ks=(0.0, 0.0, 0.0), + map_Kd=None, + map_Ks=None, + map_Bump=None, + map_Pm=None, + map_Pr=None, + map_format="jpg", + step: Optional[int] = None, + ) -> List[str]: + mtl_save_path = self.get_save_path(filename) + save_paths = [mtl_save_path] + mtl_str = f"newmtl {matname}\n" + mtl_str += f"Ka {Ka[0]} {Ka[1]} {Ka[2]}\n" + if map_Kd is not None: + map_Kd_save_path = os.path.join( + os.path.dirname(mtl_save_path), f"texture_kd.{map_format}" + ) + mtl_str += f"map_Kd texture_kd.{map_format}\n" + self._save_rgb_image( + map_Kd_save_path, + map_Kd, + data_format="HWC", + data_range=(0, 1), + name=f"{matname}_Kd", + step=step, + ) + save_paths.append(map_Kd_save_path) + else: + mtl_str += f"Kd {Kd[0]} {Kd[1]} {Kd[2]}\n" + if map_Ks is not None: + map_Ks_save_path = os.path.join( + os.path.dirname(mtl_save_path), f"texture_ks.{map_format}" + ) + mtl_str += f"map_Ks texture_ks.{map_format}\n" + self._save_rgb_image( + map_Ks_save_path, + map_Ks, + data_format="HWC", + data_range=(0, 1), + name=f"{matname}_Ks", + step=step, + ) + save_paths.append(map_Ks_save_path) + else: + mtl_str += f"Ks {Ks[0]} {Ks[1]} {Ks[2]}\n" + if map_Bump is not None: + map_Bump_save_path = os.path.join( + os.path.dirname(mtl_save_path), f"texture_nrm.{map_format}" + ) + mtl_str += f"map_Bump texture_nrm.{map_format}\n" + self._save_rgb_image( + map_Bump_save_path, + map_Bump, + data_format="HWC", + data_range=(0, 1), + name=f"{matname}_Bump", + step=step, + ) + save_paths.append(map_Bump_save_path) + if map_Pm is not None: + map_Pm_save_path = os.path.join( + os.path.dirname(mtl_save_path), f"texture_metallic.{map_format}" + ) + mtl_str += f"map_Pm texture_metallic.{map_format}\n" + self._save_grayscale_image( + map_Pm_save_path, + map_Pm, + data_range=(0, 1), + cmap=None, + name=f"{matname}_refl", + step=step, + ) + save_paths.append(map_Pm_save_path) + if map_Pr is not None: + map_Pr_save_path = os.path.join( + os.path.dirname(mtl_save_path), f"texture_roughness.{map_format}" + ) + mtl_str += f"map_Pr texture_roughness.{map_format}\n" + self._save_grayscale_image( + map_Pr_save_path, + map_Pr, + data_range=(0, 1), + cmap=None, + name=f"{matname}_Ns", + step=step, + ) + save_paths.append(map_Pr_save_path) + with open(self.get_save_path(filename), "w") as f: + f.write(mtl_str) + return save_paths + + def save_file(self, filename, src_path) -> str: + save_path = self.get_save_path(filename) + shutil.copyfile(src_path, save_path) + return save_path + + def save_json(self, filename, payload) -> str: + save_path = self.get_save_path(filename) + with open(save_path, "w") as f: + f.write(json.dumps(payload)) + return save_path diff --git a/threestudio/utils/typing.py b/threestudio/utils/typing.py new file mode 100644 index 0000000..dee9f96 --- /dev/null +++ b/threestudio/utils/typing.py @@ -0,0 +1,40 @@ +""" +This module contains type annotations for the project, using +1. Python type hints (https://docs.python.org/3/library/typing.html) for Python objects +2. jaxtyping (https://github.com/google/jaxtyping/blob/main/API.md) for PyTorch tensors + +Two types of typing checking can be used: +1. Static type checking with mypy (install with pip and enabled as the default linter in VSCode) +2. Runtime type checking with typeguard (install with pip and triggered at runtime, mainly for tensor dtype and shape checking) +""" + +# Basic types +from typing import ( + Any, + Callable, + Dict, + Iterable, + List, + Literal, + NamedTuple, + NewType, + Optional, + Sized, + Tuple, + Type, + TypeVar, + Union, +) + +# Tensor dtype +# for jaxtyping usage, see https://github.com/google/jaxtyping/blob/main/API.md +from jaxtyping import Bool, Complex, Float, Inexact, Int, Integer, Num, Shaped, UInt + +# Config type +from omegaconf import DictConfig + +# PyTorch Tensor type +from torch import Tensor + +# Runtime type checking decorator +from typeguard import typechecked as typechecker